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href="https://gadz.academia.edu/">Ecole Nationale Supérieure arts et metiers</a>, <a class="u-tcGrayDarker" href="https://gadz.academia.edu/Departments/Paris/Documents">Paris</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Fakhreddine" data-follow-user-id="263837202" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="263837202"><span class="material-symbols-outlined" style="font-size: 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class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Fakhreddine Ababsa</h3></div><div class="js-work-strip profile--work_container" data-work-id="111609821"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set"><img alt="Research paper thumbnail of Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set">Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">3D face tracking using one monocular camera is an important topic, since it is useful in many dom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609821"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609821"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609821; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609821]").text(description); $(".js-view-count[data-work-id=111609821]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609821; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609821']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609821, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111609821]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609821,"title":"Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set","translated_title":"","metadata":{"abstract":"3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.","publication_date":{"day":1,"month":7,"year":2017,"errors":{}}},"translated_abstract":"3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.","internal_url":"https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set","translated_internal_url":"","created_at":"2023-12-16T23:55:59.827-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision"},{"id":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":822358,"name":"Ground Truth","url":"https://www.academia.edu/Documents/in/Ground_Truth"}],"urls":[{"id":37229940,"url":"https://doi.org/10.1109/iv.2017.40"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609820"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609820/Shape_related_constraints_aware_generation_of_Mechanical_Designs_through_Deep_Convolutional_GAN"><img alt="Research paper thumbnail of Shape related constraints aware generation of Mechanical Designs through Deep Convolutional GAN" class="work-thumbnail" src="https://attachments.academia-assets.com/109098709/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609820/Shape_related_constraints_aware_generation_of_Mechanical_Designs_through_Deep_Convolutional_GAN">Shape related constraints aware generation of Mechanical Designs through Deep Convolutional GAN</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 22, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f71406dfe6546091ea56a2310a847033" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098709,"asset_id":111609820,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098709/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609820"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609820"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609820; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609820]").text(description); $(".js-view-count[data-work-id=111609820]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609820; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609820']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609820, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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Mechanical design hence should rely on robust and fast tools to explore complex shapes, typically for design for additive manufacturing (DfAM). Topology optimization is such a powerful tool, yet integrating geometric constraints (shape-related) into it is hard. In this work, we leverage machine learning capability to handle complex geometric and spatial correlations to integrate into the mechanical design process geometry-related constraints at the conceptual level. More precisely, we explore the generative capabilities of recent Deep Learning architectures to enhance mechanical designs, typically for additive manufacturing. In this work, we build a generative Deep-Learning-based approach of topology optimization integrating mechanical conditions in addition to one typical manufacturing condition (the complexity of a design i.e. a geometrical condition). The approach is a dual-discriminator GAN: a generator that takes as input the mechanical and geometrical conditions and outputs a 2D structure and two discriminators, one to ensure that the generated structure follows the mechanical constraints and the other to assess the geometrical constraint. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609817"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609817/Evaluating_Added_Value_of_Augmented_Reality_to_Assist_Aeronautical_Maintenance_Workers_Experimentation_on_On_field_Use_Case"><img alt="Research paper thumbnail of Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers—Experimentation on On-field Use Case" class="work-thumbnail" src="https://attachments.academia-assets.com/109098772/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609817/Evaluating_Added_Value_of_Augmented_Reality_to_Assist_Aeronautical_Maintenance_Workers_Experimentation_on_On_field_Use_Case">Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers—Experimentation on On-field Use Case</a></div><div class="wp-workCard_item"><span>Virtual Reality and Augmented Reality</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8ba014367da6135276fead4ce09a5cc0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098772,"asset_id":111609817,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098772/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609817"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609817"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609817; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609816"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609816/Pedestrian_Using_Catadioptric_Sensor_12"><img alt="Research paper thumbnail of Pedestrian Using Catadioptric Sensor 12" class="work-thumbnail" src="https://attachments.academia-assets.com/109098687/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609816/Pedestrian_Using_Catadioptric_Sensor_12">Pedestrian Using Catadioptric Sensor 12</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the detection of person in the omnidirectional images, adopting a linear SVM. We h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the detection of person in the omnidirectional images, adopting a linear SVM. We have implemented HOG-based descriptors, for omnidirectional and spherical images. In this paper we studied the influence of each parameter in our algorithm on the performances of person detections in catadioptric images. However, few studies have elaborated the problem of human detection using this type of cameras; therefore we have set up our own test base. Our results show that our detector can robustly detect people in omnidirectional images, as soon as the algorithm is adapted to the distortions introduced by the use of the omnidirectional camera.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2d08d36df00d97618e86dc94747af02f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098687,"asset_id":111609816,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098687/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609816"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609816"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609816; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609816]").text(description); $(".js-view-count[data-work-id=111609816]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609816; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609816']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609816, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2d08d36df00d97618e86dc94747af02f" } } $('.js-work-strip[data-work-id=111609816]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609816,"title":"Pedestrian Using Catadioptric Sensor 12","translated_title":"","metadata":{"abstract":"We investigate the detection of person in the omnidirectional images, adopting a linear SVM. 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609812/Usability_of_Augmented_Reality_in_Aeronautic_Maintenance_Repair_and_Overhaul"><img alt="Research paper thumbnail of Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul" class="work-thumbnail" src="https://attachments.academia-assets.com/109098694/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609812/Usability_of_Augmented_Reality_in_Aeronautic_Maintenance_Repair_and_Overhaul">Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Augmented Reality (AR) is a strong growing research topic in several areas including industry, tr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Augmented Reality (AR) is a strong growing research topic in several areas including industry, training, art and entertainment. AR can help users to achieve very complex tasks by enhancing their vision with useful and well-adapted information. This paper deals with evaluating the usability of AR in aeronautic maintenance training tasks. A case study in the on-site maintenance department was conducted using an augmented reality application, involving operators at several levels of expertise. Obtained results highlighted the full efficacy of AR in the field of aeronautic maintenance. CCS Concepts • Human-centered computing → Mixed / augmented reality;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8347157cb8c47b51c9486658ecb28495" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098694,"asset_id":111609812,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098694/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609812"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609812"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609812; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609812]").text(description); $(".js-view-count[data-work-id=111609812]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609812; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609812']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609812, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8347157cb8c47b51c9486658ecb28495" } } $('.js-work-strip[data-work-id=111609812]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609812,"title":"Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul","translated_title":"","metadata":{"abstract":"Augmented Reality (AR) is a strong growing research topic in several areas including industry, training, art and entertainment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609811"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609811/Free_Hand_Based_3D_Interaction_in_Optical_See_Through_Augmented_Reality_Using_Leap_Motion"><img alt="Research paper thumbnail of Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion" class="work-thumbnail" src="https://attachments.academia-assets.com/109098696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609811/Free_Hand_Based_3D_Interaction_in_Optical_See_Through_Augmented_Reality_Using_Leap_Motion">Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In augmented reality environments, the natural hand interaction between a virtual object and the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In augmented reality environments, the natural hand interaction between a virtual object and the user is a major issue to manipulate a rendered object in a convenient way. Microsoft’s HoloLens (Microsoft 2018) is an innovative augmented reality (AR) device that has provided an impressive experience for the user. However, the gesture interactions offered to the user are very limited. HoloLens currently recognizes two core component gestures: Air tap and Bloom. To solve this issue, we propose to integrate a Leap Motion Controller (LMC) within the HoloLens device (Figure 1). We thus used 3D hand and finger tracking provided by the LMC (Lu & al., 2016) to propose new free hand-based interaction more natural and intuitive. We implemented three fully 3D techniques for selection, translation and rotation manipulation. In this work, we first investigated how to combine the two devices to get them working together in real time, and then we evaluated the proposed 3D hand interactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="de7247812016efae9b19291984428968" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098696,"asset_id":111609811,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098696/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609811"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609811"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609811; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609811]").text(description); $(".js-view-count[data-work-id=111609811]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609811; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609811']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609811, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "de7247812016efae9b19291984428968" } } $('.js-work-strip[data-work-id=111609811]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609811,"title":"Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion","translated_title":"","metadata":{"abstract":"In augmented reality environments, the natural hand interaction between a virtual object and the user is a major issue to manipulate a rendered object in a convenient way. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609810"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609810/Reconnaissance_3D_des_Gestes_pour_l_Interaction_Naturelle_Homme_Robot"><img alt="Research paper thumbnail of Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot" class="work-thumbnail" src="https://attachments.academia-assets.com/109098684/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609810/Reconnaissance_3D_des_Gestes_pour_l_Interaction_Naturelle_Homme_Robot">Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction hom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction homme robot (HRI) basee sur l’information de profondeur fournie par la Kinect. Le suivi du corps est realise avec l’algorithme Skeleton fourni par le Kinect SDK. L’idee de ce travail est de calculer les angles des articulations de la partie supe- rieure du corps durant l’execution du geste. Les variations de ces angles seront les entrees des Modeles de Markov Caches afin de reconnaitre les gestes dynamiques. Les re- sultats montrent que notre methode est tres robuste ; elle necessite peu de pretraitements et n’est pas influencee par les conditions de l’environnement comme les changements d’eclairage et la complexite de la scene.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39bd8ec1c810cc4c7b6602f4e0bd7f4a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098684,"asset_id":111609810,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098684/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609810"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609810"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609810; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609810]").text(description); $(".js-view-count[data-work-id=111609810]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609810; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609810']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609810, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "39bd8ec1c810cc4c7b6602f4e0bd7f4a" } } $('.js-work-strip[data-work-id=111609810]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609810,"title":"Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot","translated_title":"","metadata":{"abstract":"Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction homme robot (HRI) basee sur l’information de profondeur fournie par la Kinect. 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L’idee de ce travail est de calculer les angles des articulations de la partie supe- rieure du corps durant l’execution du geste. Les variations de ces angles seront les entrees des Modeles de Markov Caches afin de reconnaitre les gestes dynamiques. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609808"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction"><img alt="Research paper thumbnail of Dynamic Gesture Recognition for Natural Human System Interaction" class="work-thumbnail" src="https://attachments.academia-assets.com/109098683/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction">Dynamic Gesture Recognition for Natural Human System Interaction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="14717555d7c08ea1beffea23f93dea0d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098683,"asset_id":111609808,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609808"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609808"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609808; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609808]").text(description); $(".js-view-count[data-work-id=111609808]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609808; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609808']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609808, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "14717555d7c08ea1beffea23f93dea0d" } } $('.js-work-strip[data-work-id=111609808]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609808,"title":"Dynamic Gesture Recognition for Natural Human System Interaction","translated_title":"","metadata":{"abstract":"This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. 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The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.","internal_url":"https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction","translated_internal_url":"","created_at":"2023-12-16T23:55:56.906-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098683,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098683/thumbnails/1.jpg","file_name":"17Vol91No2.pdf","download_url":"https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Gesture_Recognition_for_Natural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098683/17Vol91No2-libre.pdf?1702800359=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Gesture_Recognition_for_Natural.pdf\u0026Expires=1733201480\u0026Signature=BXy0nNhedw-iVwo5syqTro6WkOadoVbjbrkA1o0WEfsdEhKPlLMM-4akHCZDc~x92qZxIO6Yox2pRX9IYAQiJANWfWM-EHStKDvgeyViuQoV9KqZagB5Sw-cQbxJZthSzBfphbtpkxMAczyBsfv2TsgOX9UEx3ruvgS2-MD~SE5PfqOrtsXe0EDRdSKNg2SYkGCDwVPffEFJCwdisUyVAyoJz8U4onXFK1zw51anncHuddVN9XWHmtSngsajz~w~wsyXLWMRDi-ThiKjM~Vm10PyDnFy9FKqEIVjIEGcaLyYZrfZ3nwAnZKdfb8qjTZ5T5Z4tpGMSTHFIKGjRg6Brw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098683,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098683/thumbnails/1.jpg","file_name":"17Vol91No2.pdf","download_url":"https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Gesture_Recognition_for_Natural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098683/17Vol91No2-libre.pdf?1702800359=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Gesture_Recognition_for_Natural.pdf\u0026Expires=1733201480\u0026Signature=BXy0nNhedw-iVwo5syqTro6WkOadoVbjbrkA1o0WEfsdEhKPlLMM-4akHCZDc~x92qZxIO6Yox2pRX9IYAQiJANWfWM-EHStKDvgeyViuQoV9KqZagB5Sw-cQbxJZthSzBfphbtpkxMAczyBsfv2TsgOX9UEx3ruvgS2-MD~SE5PfqOrtsXe0EDRdSKNg2SYkGCDwVPffEFJCwdisUyVAyoJz8U4onXFK1zw51anncHuddVN9XWHmtSngsajz~w~wsyXLWMRDi-ThiKjM~Vm10PyDnFy9FKqEIVjIEGcaLyYZrfZ3nwAnZKdfb8qjTZ5T5Z4tpGMSTHFIKGjRg6Brw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":472,"name":"Human Computer Interaction","url":"https://www.academia.edu/Documents/in/Human_Computer_Interaction"},{"id":3147,"name":"Gesture","url":"https://www.academia.edu/Documents/in/Gesture"},{"id":17701,"name":"Gesture Recognition","url":"https://www.academia.edu/Documents/in/Gesture_Recognition"},{"id":91387,"name":"Kinect","url":"https://www.academia.edu/Documents/in/Kinect"},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model"},{"id":432494,"name":"Dynamic Time Warping","url":"https://www.academia.edu/Documents/in/Dynamic_Time_Warping"},{"id":1350274,"name":"Archaeology of Natural Places","url":"https://www.academia.edu/Documents/in/Archaeology_of_Natural_Places"}],"urls":[{"id":37229928,"url":"http://www.jatit.org/volumes/Vol91No2/17Vol91No2.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609806"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera"><img alt="Research paper thumbnail of 3D Human Tracking with Catadioptric Omnidirectional Camera" class="work-thumbnail" src="https://attachments.academia-assets.com/109098770/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera">3D Human Tracking with Catadioptric Omnidirectional Camera</a></div><div class="wp-workCard_item"><span>Proceedings of the 2019 on International Conference on Multimedia Retrieval</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6f0271e0e5bf2ef2df5e822617413195" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098770,"asset_id":111609806,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098770/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609806"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609806"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609806; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609805"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609805/Augmented_Reality_Application_in_Manufacturing_Industry_Maintenance_and_Non_destructive_Testing_NDT_Use_Cases"><img alt="Research paper thumbnail of Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases" class="work-thumbnail" src="https://attachments.academia-assets.com/109098736/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609805/Augmented_Reality_Application_in_Manufacturing_Industry_Maintenance_and_Non_destructive_Testing_NDT_Use_Cases">Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="be65ec809ef9fbfad02588ad19daac14" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098736,"asset_id":111609805,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098736/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609805"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609805"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609805; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609804"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609804/Methodology_for_the_Field_Evaluation_of_the_Impact_of_Augmented_Reality_Tools_for_Maintenance_Workers_in_the_Aeronautic_Industry"><img alt="Research paper thumbnail of Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry" class="work-thumbnail" src="https://attachments.academia-assets.com/109098761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609804/Methodology_for_the_Field_Evaluation_of_the_Impact_of_Augmented_Reality_Tools_for_Maintenance_Workers_in_the_Aeronautic_Industry">Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry</a></div><div class="wp-workCard_item"><span>Frontiers in Virtual Reality</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. However, assessments of the actual impact of AR on industrial processes are limited due to a lack of methods and tools to assist in the integration and evaluation of AR tools in the field. This paper presents a method for deploying AR tools adapted to maintenance workers and for selecting relevant evaluation criteria of the impact in an industrial context. This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on produc...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="63ea5efb3ac6d2e7768889c65ef3c5ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098761,"asset_id":111609804,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098761/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609804"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609804"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609804; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609804]").text(description); $(".js-view-count[data-work-id=111609804]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609804; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609804']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609804, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "63ea5efb3ac6d2e7768889c65ef3c5ef" } } $('.js-work-strip[data-work-id=111609804]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609804,"title":"Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry","translated_title":"","metadata":{"abstract":"Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. 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This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on produc...","publisher":"Frontiers Media SA","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Frontiers in Virtual Reality"},"translated_abstract":"Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609803"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter"><img alt="Research paper thumbnail of 3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter" class="work-thumbnail" src="https://attachments.academia-assets.com/109098705/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter">3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this paper is to investigate the problem of 3D human tracking in complex environme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d73b96cd912a61b2eb5645e20f435689" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098705,"asset_id":111609803,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098705/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609803"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609803"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609803; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609803]").text(description); $(".js-view-count[data-work-id=111609803]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609803; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609803']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609803, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d73b96cd912a61b2eb5645e20f435689" } } $('.js-work-strip[data-work-id=111609803]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609803,"title":"3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter","translated_title":"","metadata":{"abstract":"The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. 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paper thumbnail of An Efficient Human Activity Recognition Technique Based on Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/109098776/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609802/An_Efficient_Human_Activity_Recognition_Technique_Based_on_Deep_Learning">An Efficient Human Activity Recognition Technique Based on Deep Learning</a></div><div class="wp-workCard_item"><span>Pattern Recognition and Image Analysis</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb81a65636e34f8edb82a26e73ea1a41" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609800"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition"><img alt="Research paper thumbnail of Fusion of structural and textural features for melanoma recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/109098762/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition">Fusion of structural and textural features for melanoma recognition</a></div><div class="wp-workCard_item"><span>IET Computer Vision</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Melanoma is one the most increasing cancers since past decades. For accurate detection and classi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bd0812efde7ea58cf3f98e12837292d2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098762,"asset_id":111609800,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098762/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609800"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609800"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609800; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609800]").text(description); $(".js-view-count[data-work-id=111609800]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609800; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609800']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609800, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bd0812efde7ea58cf3f98e12837292d2" } } $('.js-work-strip[data-work-id=111609800]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609800,"title":"Fusion of structural and textural features for melanoma recognition","translated_title":"","metadata":{"abstract":"Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...","publisher":"Institution of Engineering and Technology (IET)","ai_title_tag":"Melanoma Detection via Structural and Textural Feature Fusion","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"IET Computer Vision"},"translated_abstract":"Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...","internal_url":"https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition","translated_internal_url":"","created_at":"2023-12-16T23:55:55.307-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098762,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098762/thumbnails/1.jpg","file_name":"paper3.pdf","download_url":"https://www.academia.edu/attachments/109098762/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fusion_of_structural_and_textural_featur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098762/paper3-libre.pdf?1702800346=\u0026response-content-disposition=attachment%3B+filename%3DFusion_of_structural_and_textural_featur.pdf\u0026Expires=1733201481\u0026Signature=aqJHVsDbDdZfYXO4UYt7HgO1fWLWGM60OdKADjUzxARh5lVIFFfFh2tQiUFXI2BPEdFVud2VifwaaUXFJuDHSC9SO3wFrYxYPCFSN6SReIVI6Q1E6eqNvU14cmr40wb3sxUeHmQgeZmDliR3Np3J-d6DIMY5hJ7VvUgi8VNCNqeSbutUWacHQUEfa7htAyMtMvRYNYnf05jCsH-maZTWYmOOfLEoCcS4pvYAYu~ANsrUx0mxKwMoaWsX9NDHsRje-NRUzgkUjjenMchF9oH7YE-kfCsUbdL5TmDFl8MFS4L1Gcm6YlD4X1vL8srQGW41eH0uogcpaCMq6XAohCZm7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Fusion_of_structural_and_textural_features_for_melanoma_recognition","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098762,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098762/thumbnails/1.jpg","file_name":"paper3.pdf","download_url":"https://www.academia.edu/attachments/109098762/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fusion_of_structural_and_textural_featur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098762/paper3-libre.pdf?1702800346=\u0026response-content-disposition=attachment%3B+filename%3DFusion_of_structural_and_textural_featur.pdf\u0026Expires=1733201481\u0026Signature=aqJHVsDbDdZfYXO4UYt7HgO1fWLWGM60OdKADjUzxARh5lVIFFfFh2tQiUFXI2BPEdFVud2VifwaaUXFJuDHSC9SO3wFrYxYPCFSN6SReIVI6Q1E6eqNvU14cmr40wb3sxUeHmQgeZmDliR3Np3J-d6DIMY5hJ7VvUgi8VNCNqeSbutUWacHQUEfa7htAyMtMvRYNYnf05jCsH-maZTWYmOOfLEoCcS4pvYAYu~ANsrUx0mxKwMoaWsX9NDHsRje-NRUzgkUjjenMchF9oH7YE-kfCsUbdL5TmDFl8MFS4L1Gcm6YlD4X1vL8srQGW41eH0uogcpaCMq6XAohCZm7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":4998,"name":"Medical Image Processing","url":"https://www.academia.edu/Documents/in/Medical_Image_Processing"},{"id":6021,"name":"Cancer","url":"https://www.academia.edu/Documents/in/Cancer"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":83038,"name":"Image fusion","url":"https://www.academia.edu/Documents/in/Image_fusion"},{"id":91365,"name":"Wavelet Transforms","url":"https://www.academia.edu/Documents/in/Wavelet_Transforms"},{"id":150094,"name":"Fusion","url":"https://www.academia.edu/Documents/in/Fusion"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction"},{"id":167397,"name":"Image recognition","url":"https://www.academia.edu/Documents/in/Image_recognition"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":403692,"name":"Curvelet","url":"https://www.academia.edu/Documents/in/Curvelet"},{"id":457105,"name":"Dermoscopy","url":"https://www.academia.edu/Documents/in/Dermoscopy"},{"id":1010893,"name":"Cancers","url":"https://www.academia.edu/Documents/in/Cancers"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":1372175,"name":"Textural Features","url":"https://www.academia.edu/Documents/in/Textural_Features"},{"id":1991646,"name":"Local Binary Patterns","url":"https://www.academia.edu/Documents/in/Local_Binary_Patterns"},{"id":2571968,"name":"Structural Features","url":"https://www.academia.edu/Documents/in/Structural_Features"}],"urls":[{"id":37229920,"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2017.0193"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609798"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609798/Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies"><img alt="Research paper thumbnail of Towards improving the future of manufacturing through digital twin and augmented reality technologies" class="work-thumbnail" src="https://attachments.academia-assets.com/109098735/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609798/Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies">Towards improving the future of manufacturing through digital twin and augmented reality technologies</a></div><div class="wp-workCard_item"><span>Procedia Manufacturing</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="154ba7982e0aa20d598f9569be034f28" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098735,"asset_id":111609798,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609798"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609798"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609798; 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In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Procedia Manufacturing","grobid_abstract_attachment_id":109098735},"translated_abstract":null,"internal_url":"https://www.academia.edu/111609798/Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies","translated_internal_url":"","created_at":"2023-12-16T23:55:55.015-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098735,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098735/thumbnails/1.jpg","file_name":"LE2I_PROCMANUFACTURING_2018_ABABSA.pdf","download_url":"https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_improving_the_future_of_manufact.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098735/LE2I_PROCMANUFACTURING_2018_ABABSA-libre.pdf?1702800352=\u0026response-content-disposition=attachment%3B+filename%3DTowards_improving_the_future_of_manufact.pdf\u0026Expires=1733201481\u0026Signature=YXGiWDBEdUZ7EHV-yttnMDAFeKUc329CtV9lPWw9d-j75ONsgNC~mNx9Jl6yeADOYaIfqTmVXYBlkxP3VrqxonVDuLFEtr1lRVmD0ZrL4YvY3FfsHIK4EkcUhzs5v1Sb4Bv2h1GFHPSoipMSjfBrJfHJlcXF7bzllkfjEmiLEA2SuB4Tv4YJ6aRPb~-unqVC29W74rjPzHJJn140RYjEDgMG-cQbhVHZtyHmB8w9w~Qd6sIrtW5YAPJm9LhCx1Ckhfqd8xJtvPCkgtM43CWNkaU45tWh93a9NV2Z5Ytqajuoz7tN-cEFCdLtjkrWa9RGH~hcsIsIuuHWzK5GMnkfmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098735,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098735/thumbnails/1.jpg","file_name":"LE2I_PROCMANUFACTURING_2018_ABABSA.pdf","download_url":"https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_improving_the_future_of_manufact.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098735/LE2I_PROCMANUFACTURING_2018_ABABSA-libre.pdf?1702800352=\u0026response-content-disposition=attachment%3B+filename%3DTowards_improving_the_future_of_manufact.pdf\u0026Expires=1733201481\u0026Signature=YXGiWDBEdUZ7EHV-yttnMDAFeKUc329CtV9lPWw9d-j75ONsgNC~mNx9Jl6yeADOYaIfqTmVXYBlkxP3VrqxonVDuLFEtr1lRVmD0ZrL4YvY3FfsHIK4EkcUhzs5v1Sb4Bv2h1GFHPSoipMSjfBrJfHJlcXF7bzllkfjEmiLEA2SuB4Tv4YJ6aRPb~-unqVC29W74rjPzHJJn140RYjEDgMG-cQbhVHZtyHmB8w9w~Qd6sIrtW5YAPJm9LhCx1Ckhfqd8xJtvPCkgtM43CWNkaU45tWh93a9NV2Z5Ytqajuoz7tN-cEFCdLtjkrWa9RGH~hcsIsIuuHWzK5GMnkfmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":5673,"name":"Augmented Reality","url":"https://www.academia.edu/Documents/in/Augmented_Reality"},{"id":8910,"name":"Evaluation","url":"https://www.academia.edu/Documents/in/Evaluation"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":66379,"name":"Automation","url":"https://www.academia.edu/Documents/in/Automation"},{"id":85280,"name":"Industry","url":"https://www.academia.edu/Documents/in/Industry"},{"id":167727,"name":"Industrial Revolution","url":"https://www.academia.edu/Documents/in/Industrial_Revolution"},{"id":511079,"name":"Predictive Maintenance","url":"https://www.academia.edu/Documents/in/Predictive_Maintenance"},{"id":2741797,"name":"Digital Twin","url":"https://www.academia.edu/Documents/in/Digital_Twin"}],"urls":[{"id":37229918,"url":"https://api.elsevier.com/content/article/PII:S2351978918311867?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609797"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609797/A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data"><img alt="Research paper thumbnail of A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609797/A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data">A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper propose a new method for wide-rotation non-rigid face tracking that is still a challen...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to $90^{\circ }$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than $4^o$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. These results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609797"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609797"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609797; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609797]").text(description); $(".js-view-count[data-work-id=111609797]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609797; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609797']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609797, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111609797]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609797,"title":"A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data","translated_title":"","metadata":{"abstract":"This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to \$90^{\\circ }\$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than \$4^o\$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. These results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to \$90^{\\circ }\$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than \$4^o\$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. These results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.","internal_url":"https://www.academia.edu/111609797/A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data","translated_internal_url":"","created_at":"2023-12-16T23:55:54.859-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609796"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609796/A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition"><img alt="Research paper thumbnail of A Depth-based Approach for 3D Dynamic Gesture Recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/109098734/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609796/A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition">A Depth-based Approach for 3D Dynamic Gesture Recognition</a></div><div class="wp-workCard_item"><span>Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4df3f8ee5842f036fc71a74263db4bd6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098734,"asset_id":111609796,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098734/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609796"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609796"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609796; 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Results demonstrate the robustness of our method against environmental conditions such as illumination changes and scene complexity due to using depth information only.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics","grobid_abstract_attachment_id":109098734},"translated_abstract":null,"internal_url":"https://www.academia.edu/111609796/A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition","translated_internal_url":"","created_at":"2023-12-16T23:55:54.710-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098734,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098734/thumbnails/1.jpg","file_name":"Hajar_HIYADI_ICINCO.pdf","download_url":"https://www.academia.edu/attachments/109098734/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Depth_based_Approach_for_3D_Dynamic_Ge.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098734/Hajar_HIYADI_ICINCO-libre.pdf?1702800346=\u0026response-content-disposition=attachment%3B+filename%3DA_Depth_based_Approach_for_3D_Dynamic_Ge.pdf\u0026Expires=1733201481\u0026Signature=L~kPLqozZvyb5RVLtdAXeSwUNTUjErE3ait5vWmWdT0LqCduGPcCWBF~JGJh~U5KGe-iYaBkRFIVmgDY93puOGZxISqotzbs3lfIqPi7BFbYj-coOlhow6M0Bh~xUBAF154hwQLcsbyxzEk1zSNcxI0MIOQ4YPk0Lj5xzeoIzzjLsV1JhIPGb6B9VXFr1QeQmZCmB5ggYDJiTVlAdOoiEIBY0Pp9ddDnMUQUr-e3DnO-hCPjqXfr9p-40QPV9hHOPMnU~pSozFS7F7cwWbiBe8U6dLcdn7w8rQeWBhfJAgS8EI9P2NWnHBMpUSM4mcQ-RtnQmLJULyiIamzkEx-lxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098734,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098734/thumbnails/1.jpg","file_name":"Hajar_HIYADI_ICINCO.pdf","download_url":"https://www.academia.edu/attachments/109098734/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Depth_based_Approach_for_3D_Dynamic_Ge.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098734/Hajar_HIYADI_ICINCO-libre.pdf?1702800346=\u0026response-content-disposition=attachment%3B+filename%3DA_Depth_based_Approach_for_3D_Dynamic_Ge.pdf\u0026Expires=1733201481\u0026Signature=L~kPLqozZvyb5RVLtdAXeSwUNTUjErE3ait5vWmWdT0LqCduGPcCWBF~JGJh~U5KGe-iYaBkRFIVmgDY93puOGZxISqotzbs3lfIqPi7BFbYj-coOlhow6M0Bh~xUBAF154hwQLcsbyxzEk1zSNcxI0MIOQ4YPk0Lj5xzeoIzzjLsV1JhIPGb6B9VXFr1QeQmZCmB5ggYDJiTVlAdOoiEIBY0Pp9ddDnMUQUr-e3DnO-hCPjqXfr9p-40QPV9hHOPMnU~pSozFS7F7cwWbiBe8U6dLcdn7w8rQeWBhfJAgS8EI9P2NWnHBMpUSM4mcQ-RtnQmLJULyiIamzkEx-lxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision"},{"id":3147,"name":"Gesture","url":"https://www.academia.edu/Documents/in/Gesture"},{"id":17701,"name":"Gesture Recognition","url":"https://www.academia.edu/Documents/in/Gesture_Recognition"},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="17423315" id="papers"><div class="js-work-strip profile--work_container" data-work-id="111609821"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set"><img alt="Research paper thumbnail of Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set">Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">3D face tracking using one monocular camera is an important topic, since it is useful in many dom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609821"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609821"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609821; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609821]").text(description); $(".js-view-count[data-work-id=111609821]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609821; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609821']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609821, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111609821]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609821,"title":"Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set","translated_title":"","metadata":{"abstract":"3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.","publication_date":{"day":1,"month":7,"year":2017,"errors":{}}},"translated_abstract":"3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.","internal_url":"https://www.academia.edu/111609821/Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set","translated_internal_url":"","created_at":"2023-12-16T23:55:59.827-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Challenging_3D_Head_Tracking_and_Evaluation_Using_Unconstrained_Test_Data_Set","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision"},{"id":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":822358,"name":"Ground Truth","url":"https://www.academia.edu/Documents/in/Ground_Truth"}],"urls":[{"id":37229940,"url":"https://doi.org/10.1109/iv.2017.40"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609820"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609820/Shape_related_constraints_aware_generation_of_Mechanical_Designs_through_Deep_Convolutional_GAN"><img alt="Research paper thumbnail of Shape related constraints aware generation of Mechanical Designs through Deep Convolutional GAN" class="work-thumbnail" src="https://attachments.academia-assets.com/109098709/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609820/Shape_related_constraints_aware_generation_of_Mechanical_Designs_through_Deep_Convolutional_GAN">Shape related constraints aware generation of Mechanical Designs through Deep Convolutional GAN</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 22, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f71406dfe6546091ea56a2310a847033" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098709,"asset_id":111609820,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098709/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609820"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609820"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609820; 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Mechanical design hence should rely on robust and fast tools to explore complex shapes, typically for design for additive manufacturing (DfAM). Topology optimization is such a powerful tool, yet integrating geometric constraints (shape-related) into it is hard. In this work, we leverage machine learning capability to handle complex geometric and spatial correlations to integrate into the mechanical design process geometry-related constraints at the conceptual level. More precisely, we explore the generative capabilities of recent Deep Learning architectures to enhance mechanical designs, typically for additive manufacturing. In this work, we build a generative Deep-Learning-based approach of topology optimization integrating mechanical conditions in addition to one typical manufacturing condition (the complexity of a design i.e. a geometrical condition). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609818"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609818/A_Simple_Human_Activity_Recognition_Technique_Using_DCT"><img alt="Research paper thumbnail of A Simple Human Activity Recognition Technique Using DCT" class="work-thumbnail" src="https://attachments.academia-assets.com/109098693/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609818/A_Simple_Human_Activity_Recognition_Technique_Using_DCT">A Simple Human Activity Recognition Technique Using DCT</a></div><div class="wp-workCard_item"><span>Advanced Concepts for Intelligent Vision Systems</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="da65baa95a9ce7e6189c19f9fb2160ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098693,"asset_id":111609818,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098693/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609818"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609818"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609818; 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The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Advanced Concepts for Intelligent Vision Systems","grobid_abstract_attachment_id":109098693},"translated_abstract":null,"internal_url":"https://www.academia.edu/111609818/A_Simple_Human_Activity_Recognition_Technique_Using_DCT","translated_internal_url":"","created_at":"2023-12-16T23:55:59.363-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098693,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098693/thumbnails/1.jpg","file_name":"978-3-319-48680-2_4.pdf","download_url":"https://www.academia.edu/attachments/109098693/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Simple_Human_Activity_Recognition_Tech.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098693/978-3-319-48680-2_4-libre.pdf?1702800351=\u0026response-content-disposition=attachment%3B+filename%3DA_Simple_Human_Activity_Recognition_Tech.pdf\u0026Expires=1733201480\u0026Signature=EQt6B6FUsuPHteYqGUdSVSZIszIXjIQCwtKa8yLvX3VGd6QYthB5RJDB4nO0Zswe1bAEacBdr~V7WkmpRAvIB2DEEICukxAGiON7q0p~kAsvLXaFFV07yL1xA7THgYvRooQHc0JIbP9C85YWDhgGWDX~70MTZJibSGHvQypsefIELyTcIzz6Yw~PSTYp3lCCg7s3V4aygBEJtfmAOFB0hTPh5G3AA~F48RLu3Pg3lL9N1u74z1uIjeOdMc-O~LG7WE251NfcFaShrE9uzDNGnzPCYxYxZHQRkcTxrNxryfGpyTj7fffvx3tdC7FEzEMd-od~-Q4wkGVwB1HxEtyfnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Simple_Human_Activity_Recognition_Technique_Using_DCT","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098693,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098693/thumbnails/1.jpg","file_name":"978-3-319-48680-2_4.pdf","download_url":"https://www.academia.edu/attachments/109098693/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Simple_Human_Activity_Recognition_Tech.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098693/978-3-319-48680-2_4-libre.pdf?1702800351=\u0026response-content-disposition=attachment%3B+filename%3DA_Simple_Human_Activity_Recognition_Tech.pdf\u0026Expires=1733201480\u0026Signature=EQt6B6FUsuPHteYqGUdSVSZIszIXjIQCwtKa8yLvX3VGd6QYthB5RJDB4nO0Zswe1bAEacBdr~V7WkmpRAvIB2DEEICukxAGiON7q0p~kAsvLXaFFV07yL1xA7THgYvRooQHc0JIbP9C85YWDhgGWDX~70MTZJibSGHvQypsefIELyTcIzz6Yw~PSTYp3lCCg7s3V4aygBEJtfmAOFB0hTPh5G3AA~F48RLu3Pg3lL9N1u74z1uIjeOdMc-O~LG7WE251NfcFaShrE9uzDNGnzPCYxYxZHQRkcTxrNxryfGpyTj7fffvx3tdC7FEzEMd-od~-Q4wkGVwB1HxEtyfnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":109098702,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098702/thumbnails/1.jpg","file_name":"978-3-319-48680-2_4.pdf","download_url":"https://www.academia.edu/attachments/109098702/download_file","bulk_download_file_name":"A_Simple_Human_Activity_Recognition_Tech.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098702/978-3-319-48680-2_4-libre.pdf?1702800351=\u0026response-content-disposition=attachment%3B+filename%3DA_Simple_Human_Activity_Recognition_Tech.pdf\u0026Expires=1733201480\u0026Signature=HIOGJZGk33EDofYYcston5NHrqqknsS4X0LmJujQLBwCcLK6yYK1TFKoK-WtnRyYmkVPxnsdV5QciGESe5~NrlPt1eclMavANOZe~1zIbjaVswUUym0ITGO0DJwCCsvcB-f1F8WsTSYXLS7O4XOKQ7FYWurnM60i8zrjq-nm8loGJixck1NgD6juSqn9MSgloRdd-Zd~4RekYP2P3lXqzh~9pUirI6a4xbQIknKg65CPMP2dyx-fVk8sItMd6g26rVBoSX1JasJ5Tz0T5QusgpUYSFwe5UIVElbCWpdGxeMX4D8KHyvMAwqp4cVVPBfFhx3b0dCa2d~VtHhwbyjpWQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":185596,"name":"Discrete Cosine Transform","url":"https://www.academia.edu/Documents/in/Discrete_Cosine_Transform"}],"urls":[{"id":37229937,"url":"http://link.springer.com/content/pdf/10.1007/978-3-319-48680-2_4"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609817"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609817/Evaluating_Added_Value_of_Augmented_Reality_to_Assist_Aeronautical_Maintenance_Workers_Experimentation_on_On_field_Use_Case"><img alt="Research paper thumbnail of Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers—Experimentation on On-field Use Case" class="work-thumbnail" src="https://attachments.academia-assets.com/109098772/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609817/Evaluating_Added_Value_of_Augmented_Reality_to_Assist_Aeronautical_Maintenance_Workers_Experimentation_on_On_field_Use_Case">Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers—Experimentation on On-field Use Case</a></div><div class="wp-workCard_item"><span>Virtual Reality and Augmented Reality</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8ba014367da6135276fead4ce09a5cc0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098772,"asset_id":111609817,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098772/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609817"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609817"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609817; 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Aeronautical Maintenance combines complexity induced by the variety of products and constraints associated to aeronautic sector and the environment of maintenance. AR tools seem well indicated to solve constraints of productivity and quality on the aeronautical maintenance activities by simplifying data interactions for the workers. However, few evaluations of AR have been done in real processes due to the difficulty of integrating the technology without proper tools for deployment and assessing the results. This paper proposes a method to select suitable criteria for AR evaluation in industrial environment and to deploy AR solutions suited to assist maintenance workers. These are used to set up on-field experiments that demonstrate benefits of AR on process and user point of view for different profiles of workers. Further work will consist on using these elements to extend results to AR evaluation on the whole aeronautical maintenance process. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609816"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609816/Pedestrian_Using_Catadioptric_Sensor_12"><img alt="Research paper thumbnail of Pedestrian Using Catadioptric Sensor 12" class="work-thumbnail" src="https://attachments.academia-assets.com/109098687/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609816/Pedestrian_Using_Catadioptric_Sensor_12">Pedestrian Using Catadioptric Sensor 12</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the detection of person in the omnidirectional images, adopting a linear SVM. We h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the detection of person in the omnidirectional images, adopting a linear SVM. We have implemented HOG-based descriptors, for omnidirectional and spherical images. In this paper we studied the influence of each parameter in our algorithm on the performances of person detections in catadioptric images. However, few studies have elaborated the problem of human detection using this type of cameras; therefore we have set up our own test base. Our results show that our detector can robustly detect people in omnidirectional images, as soon as the algorithm is adapted to the distortions introduced by the use of the omnidirectional camera.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2d08d36df00d97618e86dc94747af02f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098687,"asset_id":111609816,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098687/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609816"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609816"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609816; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609816]").text(description); $(".js-view-count[data-work-id=111609816]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609816; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609816']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609816, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2d08d36df00d97618e86dc94747af02f" } } $('.js-work-strip[data-work-id=111609816]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609816,"title":"Pedestrian Using Catadioptric Sensor 12","translated_title":"","metadata":{"abstract":"We investigate the detection of person in the omnidirectional images, adopting a linear SVM. 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609812/Usability_of_Augmented_Reality_in_Aeronautic_Maintenance_Repair_and_Overhaul"><img alt="Research paper thumbnail of Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul" class="work-thumbnail" src="https://attachments.academia-assets.com/109098694/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609812/Usability_of_Augmented_Reality_in_Aeronautic_Maintenance_Repair_and_Overhaul">Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Augmented Reality (AR) is a strong growing research topic in several areas including industry, tr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Augmented Reality (AR) is a strong growing research topic in several areas including industry, training, art and entertainment. AR can help users to achieve very complex tasks by enhancing their vision with useful and well-adapted information. This paper deals with evaluating the usability of AR in aeronautic maintenance training tasks. A case study in the on-site maintenance department was conducted using an augmented reality application, involving operators at several levels of expertise. Obtained results highlighted the full efficacy of AR in the field of aeronautic maintenance. CCS Concepts • Human-centered computing → Mixed / augmented reality;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8347157cb8c47b51c9486658ecb28495" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098694,"asset_id":111609812,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098694/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609812"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609812"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609812; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609812]").text(description); $(".js-view-count[data-work-id=111609812]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609812; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609812']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609812, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8347157cb8c47b51c9486658ecb28495" } } $('.js-work-strip[data-work-id=111609812]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609812,"title":"Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul","translated_title":"","metadata":{"abstract":"Augmented Reality (AR) is a strong growing research topic in several areas including industry, training, art and entertainment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609811"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609811/Free_Hand_Based_3D_Interaction_in_Optical_See_Through_Augmented_Reality_Using_Leap_Motion"><img alt="Research paper thumbnail of Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion" class="work-thumbnail" src="https://attachments.academia-assets.com/109098696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609811/Free_Hand_Based_3D_Interaction_in_Optical_See_Through_Augmented_Reality_Using_Leap_Motion">Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In augmented reality environments, the natural hand interaction between a virtual object and the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In augmented reality environments, the natural hand interaction between a virtual object and the user is a major issue to manipulate a rendered object in a convenient way. Microsoft’s HoloLens (Microsoft 2018) is an innovative augmented reality (AR) device that has provided an impressive experience for the user. However, the gesture interactions offered to the user are very limited. HoloLens currently recognizes two core component gestures: Air tap and Bloom. To solve this issue, we propose to integrate a Leap Motion Controller (LMC) within the HoloLens device (Figure 1). We thus used 3D hand and finger tracking provided by the LMC (Lu & al., 2016) to propose new free hand-based interaction more natural and intuitive. We implemented three fully 3D techniques for selection, translation and rotation manipulation. In this work, we first investigated how to combine the two devices to get them working together in real time, and then we evaluated the proposed 3D hand interactions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="de7247812016efae9b19291984428968" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098696,"asset_id":111609811,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098696/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609811"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609811"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609811; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609811]").text(description); $(".js-view-count[data-work-id=111609811]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609811; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609811']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609811, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "de7247812016efae9b19291984428968" } } $('.js-work-strip[data-work-id=111609811]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609811,"title":"Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion","translated_title":"","metadata":{"abstract":"In augmented reality environments, the natural hand interaction between a virtual object and the user is a major issue to manipulate a rendered object in a convenient way. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609810"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609810/Reconnaissance_3D_des_Gestes_pour_l_Interaction_Naturelle_Homme_Robot"><img alt="Research paper thumbnail of Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot" class="work-thumbnail" src="https://attachments.academia-assets.com/109098684/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609810/Reconnaissance_3D_des_Gestes_pour_l_Interaction_Naturelle_Homme_Robot">Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction hom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction homme robot (HRI) basee sur l’information de profondeur fournie par la Kinect. Le suivi du corps est realise avec l’algorithme Skeleton fourni par le Kinect SDK. L’idee de ce travail est de calculer les angles des articulations de la partie supe- rieure du corps durant l’execution du geste. Les variations de ces angles seront les entrees des Modeles de Markov Caches afin de reconnaitre les gestes dynamiques. Les re- sultats montrent que notre methode est tres robuste ; elle necessite peu de pretraitements et n’est pas influencee par les conditions de l’environnement comme les changements d’eclairage et la complexite de la scene.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39bd8ec1c810cc4c7b6602f4e0bd7f4a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098684,"asset_id":111609810,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098684/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609810"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609810"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609810; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609810]").text(description); $(".js-view-count[data-work-id=111609810]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609810; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609810']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609810, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "39bd8ec1c810cc4c7b6602f4e0bd7f4a" } } $('.js-work-strip[data-work-id=111609810]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609810,"title":"Reconnaissance 3D des Gestes pour l’Interaction Naturelle Homme Robot","translated_title":"","metadata":{"abstract":"Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction homme robot (HRI) basee sur l’information de profondeur fournie par la Kinect. Le suivi du corps est realise avec l’algorithme Skeleton fourni par le Kinect SDK. L’idee de ce travail est de calculer les angles des articulations de la partie supe- rieure du corps durant l’execution du geste. Les variations de ces angles seront les entrees des Modeles de Markov Caches afin de reconnaitre les gestes dynamiques. Les re- sultats montrent que notre methode est tres robuste ; elle necessite peu de pretraitements et n’est pas influencee par les conditions de l’environnement comme les changements d’eclairage et la complexite de la scene.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}}},"translated_abstract":"Dans ce papier, nous proposons une methode de reconnaissance 3D des gestes pour l’interaction homme robot (HRI) basee sur l’information de profondeur fournie par la Kinect. Le suivi du corps est realise avec l’algorithme Skeleton fourni par le Kinect SDK. L’idee de ce travail est de calculer les angles des articulations de la partie supe- rieure du corps durant l’execution du geste. Les variations de ces angles seront les entrees des Modeles de Markov Caches afin de reconnaitre les gestes dynamiques. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609808"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction"><img alt="Research paper thumbnail of Dynamic Gesture Recognition for Natural Human System Interaction" class="work-thumbnail" src="https://attachments.academia-assets.com/109098683/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction">Dynamic Gesture Recognition for Natural Human System Interaction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="14717555d7c08ea1beffea23f93dea0d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098683,"asset_id":111609808,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609808"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609808"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609808; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609808]").text(description); $(".js-view-count[data-work-id=111609808]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609808; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609808']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609808, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "14717555d7c08ea1beffea23f93dea0d" } } $('.js-work-strip[data-work-id=111609808]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609808,"title":"Dynamic Gesture Recognition for Natural Human System Interaction","translated_title":"","metadata":{"abstract":"This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.","internal_url":"https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction","translated_internal_url":"","created_at":"2023-12-16T23:55:56.906-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098683,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098683/thumbnails/1.jpg","file_name":"17Vol91No2.pdf","download_url":"https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Gesture_Recognition_for_Natural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098683/17Vol91No2-libre.pdf?1702800359=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Gesture_Recognition_for_Natural.pdf\u0026Expires=1733201480\u0026Signature=BXy0nNhedw-iVwo5syqTro6WkOadoVbjbrkA1o0WEfsdEhKPlLMM-4akHCZDc~x92qZxIO6Yox2pRX9IYAQiJANWfWM-EHStKDvgeyViuQoV9KqZagB5Sw-cQbxJZthSzBfphbtpkxMAczyBsfv2TsgOX9UEx3ruvgS2-MD~SE5PfqOrtsXe0EDRdSKNg2SYkGCDwVPffEFJCwdisUyVAyoJz8U4onXFK1zw51anncHuddVN9XWHmtSngsajz~w~wsyXLWMRDi-ThiKjM~Vm10PyDnFy9FKqEIVjIEGcaLyYZrfZ3nwAnZKdfb8qjTZ5T5Z4tpGMSTHFIKGjRg6Brw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098683,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098683/thumbnails/1.jpg","file_name":"17Vol91No2.pdf","download_url":"https://www.academia.edu/attachments/109098683/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamic_Gesture_Recognition_for_Natural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098683/17Vol91No2-libre.pdf?1702800359=\u0026response-content-disposition=attachment%3B+filename%3DDynamic_Gesture_Recognition_for_Natural.pdf\u0026Expires=1733201480\u0026Signature=BXy0nNhedw-iVwo5syqTro6WkOadoVbjbrkA1o0WEfsdEhKPlLMM-4akHCZDc~x92qZxIO6Yox2pRX9IYAQiJANWfWM-EHStKDvgeyViuQoV9KqZagB5Sw-cQbxJZthSzBfphbtpkxMAczyBsfv2TsgOX9UEx3ruvgS2-MD~SE5PfqOrtsXe0EDRdSKNg2SYkGCDwVPffEFJCwdisUyVAyoJz8U4onXFK1zw51anncHuddVN9XWHmtSngsajz~w~wsyXLWMRDi-ThiKjM~Vm10PyDnFy9FKqEIVjIEGcaLyYZrfZ3nwAnZKdfb8qjTZ5T5Z4tpGMSTHFIKGjRg6Brw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":472,"name":"Human Computer Interaction","url":"https://www.academia.edu/Documents/in/Human_Computer_Interaction"},{"id":3147,"name":"Gesture","url":"https://www.academia.edu/Documents/in/Gesture"},{"id":17701,"name":"Gesture Recognition","url":"https://www.academia.edu/Documents/in/Gesture_Recognition"},{"id":91387,"name":"Kinect","url":"https://www.academia.edu/Documents/in/Kinect"},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model"},{"id":432494,"name":"Dynamic Time Warping","url":"https://www.academia.edu/Documents/in/Dynamic_Time_Warping"},{"id":1350274,"name":"Archaeology of Natural Places","url":"https://www.academia.edu/Documents/in/Archaeology_of_Natural_Places"}],"urls":[{"id":37229928,"url":"http://www.jatit.org/volumes/Vol91No2/17Vol91No2.pdf"}]}, dispatcherData: dispatcherData }); 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Today, MR head-mounted displays like the HoloLens integrate hand gesture based interaction allowing users to take actions in MR environments. However, the proposed interactions remain limited. In this paper, we propose to combine a Leap Motion Controller (LMC) with a HoloLens in order to improve gesture interaction with virtual objects. Two main issues are presented: an interactive calibration procedure for the coupled HoloLens-LMC device and an intuitive hand-based interaction approach using LMC data in the HoloLens environment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609806"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera"><img alt="Research paper thumbnail of 3D Human Tracking with Catadioptric Omnidirectional Camera" class="work-thumbnail" src="https://attachments.academia-assets.com/109098770/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera">3D Human Tracking with Catadioptric Omnidirectional Camera</a></div><div class="wp-workCard_item"><span>Proceedings of the 2019 on International Conference on Multimedia Retrieval</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6f0271e0e5bf2ef2df5e822617413195" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098770,"asset_id":111609806,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098770/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609806"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609806"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609806; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609805"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609805/Augmented_Reality_Application_in_Manufacturing_Industry_Maintenance_and_Non_destructive_Testing_NDT_Use_Cases"><img alt="Research paper thumbnail of Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases" class="work-thumbnail" src="https://attachments.academia-assets.com/109098736/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609805/Augmented_Reality_Application_in_Manufacturing_Industry_Maintenance_and_Non_destructive_Testing_NDT_Use_Cases">Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="be65ec809ef9fbfad02588ad19daac14" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098736,"asset_id":111609805,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098736/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609805"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609805"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609805; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609804"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609804/Methodology_for_the_Field_Evaluation_of_the_Impact_of_Augmented_Reality_Tools_for_Maintenance_Workers_in_the_Aeronautic_Industry"><img alt="Research paper thumbnail of Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry" class="work-thumbnail" src="https://attachments.academia-assets.com/109098761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609804/Methodology_for_the_Field_Evaluation_of_the_Impact_of_Augmented_Reality_Tools_for_Maintenance_Workers_in_the_Aeronautic_Industry">Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry</a></div><div class="wp-workCard_item"><span>Frontiers in Virtual Reality</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. However, assessments of the actual impact of AR on industrial processes are limited due to a lack of methods and tools to assist in the integration and evaluation of AR tools in the field. This paper presents a method for deploying AR tools adapted to maintenance workers and for selecting relevant evaluation criteria of the impact in an industrial context. This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on produc...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="63ea5efb3ac6d2e7768889c65ef3c5ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098761,"asset_id":111609804,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098761/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609804"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609804"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609804; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609804]").text(description); $(".js-view-count[data-work-id=111609804]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609804; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609804']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609804, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "63ea5efb3ac6d2e7768889c65ef3c5ef" } } $('.js-work-strip[data-work-id=111609804]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609804,"title":"Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry","translated_title":"","metadata":{"abstract":"Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. 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This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on produc...","publisher":"Frontiers Media SA","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Frontiers in Virtual Reality"},"translated_abstract":"Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609803"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter"><img alt="Research paper thumbnail of 3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter" class="work-thumbnail" src="https://attachments.academia-assets.com/109098705/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter">3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this paper is to investigate the problem of 3D human tracking in complex environme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d73b96cd912a61b2eb5645e20f435689" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098705,"asset_id":111609803,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098705/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609803"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609803"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609803; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609803]").text(description); $(".js-view-count[data-work-id=111609803]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609803; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609803']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609803, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d73b96cd912a61b2eb5645e20f435689" } } $('.js-work-strip[data-work-id=111609803]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609803,"title":"3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter","translated_title":"","metadata":{"abstract":"The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Sensors"},"translated_abstract":"The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. 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paper thumbnail of An Efficient Human Activity Recognition Technique Based on Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/109098776/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609802/An_Efficient_Human_Activity_Recognition_Technique_Based_on_Deep_Learning">An Efficient Human Activity Recognition Technique Based on Deep Learning</a></div><div class="wp-workCard_item"><span>Pattern Recognition and Image Analysis</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb81a65636e34f8edb82a26e73ea1a41" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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Recognition","grobid_abstract":"In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609800"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition"><img alt="Research paper thumbnail of Fusion of structural and textural features for melanoma recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/109098762/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition">Fusion of structural and textural features for melanoma recognition</a></div><div class="wp-workCard_item"><span>IET Computer Vision</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Melanoma is one the most increasing cancers since past decades. For accurate detection and classi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bd0812efde7ea58cf3f98e12837292d2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098762,"asset_id":111609800,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098762/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609800"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609800"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609800; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609800]").text(description); $(".js-view-count[data-work-id=111609800]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609800; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609800']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609800, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bd0812efde7ea58cf3f98e12837292d2" } } $('.js-work-strip[data-work-id=111609800]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609800,"title":"Fusion of structural and textural features for melanoma recognition","translated_title":"","metadata":{"abstract":"Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...","publisher":"Institution of Engineering and Technology (IET)","ai_title_tag":"Melanoma Detection via Structural and Textural Feature Fusion","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"IET Computer Vision"},"translated_abstract":"Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609798/Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies">Towards improving the future of manufacturing through digital twin and augmented reality technologies</a></div><div class="wp-workCard_item"><span>Procedia Manufacturing</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="154ba7982e0aa20d598f9569be034f28" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098735,"asset_id":111609798,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i 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})(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "154ba7982e0aa20d598f9569be034f28" } } $('.js-work-strip[data-work-id=111609798]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609798,"title":"Towards improving the future of manufacturing through digital twin and augmented reality technologies","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Under the concept of \"Industry 4.0\", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Procedia Manufacturing","grobid_abstract_attachment_id":109098735},"translated_abstract":null,"internal_url":"https://www.academia.edu/111609798/Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies","translated_internal_url":"","created_at":"2023-12-16T23:55:55.015-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":263837202,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109098735,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098735/thumbnails/1.jpg","file_name":"LE2I_PROCMANUFACTURING_2018_ABABSA.pdf","download_url":"https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_improving_the_future_of_manufact.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098735/LE2I_PROCMANUFACTURING_2018_ABABSA-libre.pdf?1702800352=\u0026response-content-disposition=attachment%3B+filename%3DTowards_improving_the_future_of_manufact.pdf\u0026Expires=1733201481\u0026Signature=YXGiWDBEdUZ7EHV-yttnMDAFeKUc329CtV9lPWw9d-j75ONsgNC~mNx9Jl6yeADOYaIfqTmVXYBlkxP3VrqxonVDuLFEtr1lRVmD0ZrL4YvY3FfsHIK4EkcUhzs5v1Sb4Bv2h1GFHPSoipMSjfBrJfHJlcXF7bzllkfjEmiLEA2SuB4Tv4YJ6aRPb~-unqVC29W74rjPzHJJn140RYjEDgMG-cQbhVHZtyHmB8w9w~Qd6sIrtW5YAPJm9LhCx1Ckhfqd8xJtvPCkgtM43CWNkaU45tWh93a9NV2Z5Ytqajuoz7tN-cEFCdLtjkrWa9RGH~hcsIsIuuHWzK5GMnkfmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Towards_improving_the_future_of_manufacturing_through_digital_twin_and_augmented_reality_technologies","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":263837202,"first_name":"Fakhreddine","middle_initials":null,"last_name":"Ababsa","page_name":"FakhreddineAbabsa","domain_name":"gadz","created_at":"2023-03-30T04:13:56.773-07:00","display_name":"Fakhreddine Ababsa","url":"https://gadz.academia.edu/FakhreddineAbabsa"},"attachments":[{"id":109098735,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109098735/thumbnails/1.jpg","file_name":"LE2I_PROCMANUFACTURING_2018_ABABSA.pdf","download_url":"https://www.academia.edu/attachments/109098735/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_improving_the_future_of_manufact.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109098735/LE2I_PROCMANUFACTURING_2018_ABABSA-libre.pdf?1702800352=\u0026response-content-disposition=attachment%3B+filename%3DTowards_improving_the_future_of_manufact.pdf\u0026Expires=1733201481\u0026Signature=YXGiWDBEdUZ7EHV-yttnMDAFeKUc329CtV9lPWw9d-j75ONsgNC~mNx9Jl6yeADOYaIfqTmVXYBlkxP3VrqxonVDuLFEtr1lRVmD0ZrL4YvY3FfsHIK4EkcUhzs5v1Sb4Bv2h1GFHPSoipMSjfBrJfHJlcXF7bzllkfjEmiLEA2SuB4Tv4YJ6aRPb~-unqVC29W74rjPzHJJn140RYjEDgMG-cQbhVHZtyHmB8w9w~Qd6sIrtW5YAPJm9LhCx1Ckhfqd8xJtvPCkgtM43CWNkaU45tWh93a9NV2Z5Ytqajuoz7tN-cEFCdLtjkrWa9RGH~hcsIsIuuHWzK5GMnkfmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":5673,"name":"Augmented Reality","url":"https://www.academia.edu/Documents/in/Augmented_Reality"},{"id":8910,"name":"Evaluation","url":"https://www.academia.edu/Documents/in/Evaluation"},{"id":61714,"name":"Production economics","url":"https://www.academia.edu/Documents/in/Production_economics"},{"id":66379,"name":"Automation","url":"https://www.academia.edu/Documents/in/Automation"},{"id":85280,"name":"Industry","url":"https://www.academia.edu/Documents/in/Industry"},{"id":167727,"name":"Industrial Revolution","url":"https://www.academia.edu/Documents/in/Industrial_Revolution"},{"id":511079,"name":"Predictive Maintenance","url":"https://www.academia.edu/Documents/in/Predictive_Maintenance"},{"id":2741797,"name":"Digital Twin","url":"https://www.academia.edu/Documents/in/Digital_Twin"}],"urls":[{"id":37229918,"url":"https://api.elsevier.com/content/article/PII:S2351978918311867?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609797"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609797/A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data"><img alt="Research paper thumbnail of A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609797/A_Non_rigid_Face_Tracking_Method_for_Wide_Rotation_Using_Synthetic_Data">A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper propose a new method for wide-rotation non-rigid face tracking that is still a challen...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to $90^{\circ }$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than $4^o$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. These results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609797"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609797"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609797; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111609797]").text(description); $(".js-view-count[data-work-id=111609797]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111609797; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111609797']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 111609797, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111609797]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111609797,"title":"A Non-rigid Face Tracking Method for Wide Rotation Using Synthetic Data","translated_title":"","metadata":{"abstract":"This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to \$90^{\\circ }\$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than \$4^o\$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. These results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"This paper propose a new method for wide-rotation non-rigid face tracking that is still a challenging problem in computer vision community. Our method consists of training and tracking phases. In training, we propose to use a large off-line synthetic database to overcome the problem of data collection. The local appearance models are then trained using linear Support Vector Machine (SVM). In tracking, we propose a two-step approach: (i) The first step uses baseline matching for a good initialization. The matching strategy between the current frame and a set of adaptive keyframes is also involved to be recoverable in terms of failed tracking. (ii) The second step estimates the model parameters using a heuristic method via pose-wise SVMs. The combination makes our approach work robustly with wide rotation, up to \$90^{\\circ }\$ of vertical axis. In addition, our method appears to be robust even in the presence of fast movements thanks to baseline matching. Compared to state-of-the-art methods, our method shows a good compromise of rigid and non-rigid parameter accuracies. This study gives a promising perspective because of the good results in terms of pose estimation (average error is less than \$4^o\$ on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111609796"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111609796/A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition"><img alt="Research paper thumbnail of A Depth-based Approach for 3D Dynamic Gesture Recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/109098734/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111609796/A_Depth_based_Approach_for_3D_Dynamic_Gesture_Recognition">A Depth-based Approach for 3D Dynamic Gesture Recognition</a></div><div class="wp-workCard_item"><span>Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4df3f8ee5842f036fc71a74263db4bd6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109098734,"asset_id":111609796,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109098734/download_file?st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&st=MTczMzE5Nzg4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111609796"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111609796"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111609796; 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