CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: recognition technologies</title> <meta name="description" content="Search results for: recognition technologies"> <meta name="keywords" content="recognition technologies"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="recognition technologies" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="recognition technologies"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5207</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: recognition technologies</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5207</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5206</span> An Evaluation of Neural Network Efficacies for Image Recognition on Edge-AI Computer Vision Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Zhao">Jie Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Su"> Meng Su</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alexNet" title="alexNet">alexNet</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG" title=" VGG"> VGG</a>, <a href="https://publications.waset.org/abstracts/search?q=googleNet" title=" googleNet"> googleNet</a>, <a href="https://publications.waset.org/abstracts/search?q=resNet" title=" resNet"> resNet</a>, <a href="https://publications.waset.org/abstracts/search?q=Jetson%20nano" title=" Jetson nano"> Jetson nano</a>, <a href="https://publications.waset.org/abstracts/search?q=CUDA" title=" CUDA"> CUDA</a>, <a href="https://publications.waset.org/abstracts/search?q=COCO-NET" title=" COCO-NET"> COCO-NET</a>, <a href="https://publications.waset.org/abstracts/search?q=cifar10" title=" cifar10"> cifar10</a>, <a href="https://publications.waset.org/abstracts/search?q=imageNet%20large%20scale%20visual%20recognition%20challenge%20%28ILSVRC%29" title=" imageNet large scale visual recognition challenge (ILSVRC)"> imageNet large scale visual recognition challenge (ILSVRC)</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20colab" title=" google colab"> google colab</a> </p> <a href="https://publications.waset.org/abstracts/176759/an-evaluation-of-neural-network-efficacies-for-image-recognition-on-edge-ai-computer-vision-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5205</span> Possibilities, Challenges and the State of the Art of Automatic Speech Recognition in Air Traffic Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Van%20Nhan%20Nguyen">Van Nhan Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Harald%20Holone"> Harald Holone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Trafﬁc Control (ATC), such as air trafﬁc control simulation and training, monitoring live operators for with the aim of safety improvements, air trafﬁc controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this ﬁeld. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air trafﬁc control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as speciﬁc approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=asr" title=" asr"> asr</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20traffic%20control" title=" air traffic control"> air traffic control</a>, <a href="https://publications.waset.org/abstracts/search?q=atc" title=" atc"> atc</a> </p> <a href="https://publications.waset.org/abstracts/31004/possibilities-challenges-and-the-state-of-the-art-of-automatic-speech-recognition-in-air-traffic-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31004.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5204</span> Review of Speech Recognition Research on Low-Resource Languages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=XuKe%20Cao">XuKe Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews the current state of research on low-resource languages in the field of speech recognition, focusing on the challenges faced by low-resource language speech recognition, including the scarcity of data resources, the lack of linguistic resources, and the diversity of dialects and accents. The article reviews recent progress in low-resource language speech recognition, including techniques such as data augmentation, end to-end models, transfer learning, and multi-task learning. Based on the challenges currently faced, the paper also provides an outlook on future research directions. Through these studies, it is expected that the performance of speech recognition for low resource languages can be improved, promoting the widespread application and adoption of related technologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low-resource%20languages" title="low-resource languages">low-resource languages</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation%20techniques" title=" data augmentation techniques"> data augmentation techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a> </p> <a href="https://publications.waset.org/abstracts/193863/review-of-speech-recognition-research-on-low-resource-languages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5203</span> Advances in Artificial intelligence Using Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20phonetic" title=" acoustic phonetic"> acoustic phonetic</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models%20%28HMM%29" title=" hidden markov models (HMM)"> hidden markov models (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20models%20of%20speech%20recognition" title=" statistical models of speech recognition"> statistical models of speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20machine%20performance" title=" human machine performance"> human machine performance</a> </p> <a href="https://publications.waset.org/abstracts/26319/advances-in-artificial-intelligence-using-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5202</span> Video Based Automatic License Plate Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ganoun">Ali Ganoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Wesam%20Algablawi"> Wesam Algablawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wasim%20BenAnaif"> Wasim BenAnaif </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain trafﬁc and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=license%20plate%20recognition" title="license plate recognition">license plate recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition" title=" recognition"> recognition</a> </p> <a href="https://publications.waset.org/abstracts/9958/video-based-automatic-license-plate-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5201</span> Handwriting Recognition of Gurmukhi Script: A Survey of Online and Offline Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravneet%20Kaur">Ravneet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character recognition is a very interesting area of pattern recognition. From past few decades, an intensive research on character recognition for Roman, Chinese, and Japanese and Indian scripts have been reported. In this paper, a review of Handwritten Character Recognition work on Indian Script Gurmukhi is being highlighted. Most of the published papers were summarized, various methodologies were analysed and their results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmukhi%20character%20recognition" title="Gurmukhi character recognition">Gurmukhi character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=online" title=" online"> online</a>, <a href="https://publications.waset.org/abstracts/search?q=offline" title=" offline"> offline</a>, <a href="https://publications.waset.org/abstracts/search?q=HCR%20survey" title=" HCR survey"> HCR survey</a> </p> <a href="https://publications.waset.org/abstracts/46337/handwriting-recognition-of-gurmukhi-script-a-survey-of-online-and-offline-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5200</span> Specified Human Motion Recognition and Unknown Hand-Held Object Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinsiang%20Shaw">Jinsiang Shaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Pik-Hoe%20Chen"> Pik-Hoe Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Automatic%20Tracking" title="Automatic Tracking">Automatic Tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=Back%20Projection" title=" Back Projection"> Back Projection</a>, <a href="https://publications.waset.org/abstracts/search?q=Motion%20Recognition" title=" Motion Recognition"> Motion Recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoplifting" title=" Shoplifting"> Shoplifting</a> </p> <a href="https://publications.waset.org/abstracts/66866/specified-human-motion-recognition-and-unknown-hand-held-object-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66866.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5199</span> An Improved OCR Algorithm on Appearance Recognition of Electronic Components Based on Self-adaptation of Multifont Template</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhu-Qing%20Jia">Zhu-Qing Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Lin"> Tao Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20Zhou"> Tong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20character%20recognition" title="optical character recognition">optical character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20page%20identification" title=" fuzzy page identification"> fuzzy page identification</a>, <a href="https://publications.waset.org/abstracts/search?q=mutual%20correlation%20matrix" title=" mutual correlation matrix"> mutual correlation matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20self-adaptation" title=" confidence self-adaptation"> confidence self-adaptation</a> </p> <a href="https://publications.waset.org/abstracts/14322/an-improved-ocr-algorithm-on-appearance-recognition-of-electronic-components-based-on-self-adaptation-of-multifont-template" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">540</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5198</span> The Application of AI in Developing Assistive Technologies for Non-Verbal Individuals with Autism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ferah%20Tesfaye%20Admasu">Ferah Tesfaye Admasu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autism Spectrum Disorder (ASD) often presents significant communication challenges, particularly for non-verbal individuals who struggle to express their needs and emotions effectively. Assistive technologies (AT) have emerged as vital tools in enhancing communication abilities for this population. Recent advancements in artificial intelligence (AI) hold the potential to revolutionize the design and functionality of these technologies. This study explores the application of AI in developing intelligent, adaptive, and user-centered assistive technologies for non-verbal individuals with autism. Through a review of current AI-driven tools, including speech-generating devices, predictive text systems, and emotion-recognition software, this research investigates how AI can bridge communication gaps, improve engagement, and support independence. Machine learning algorithms, natural language processing (NLP), and facial recognition technologies are examined as core components in creating more personalized and responsive communication aids. The study also discusses the challenges and ethical considerations involved in deploying AI-based AT, such as data privacy and the risk of over-reliance on technology. Findings suggest that integrating AI into assistive technologies can significantly enhance the quality of life for non-verbal individuals with autism, providing them with greater opportunities for social interaction and participation in daily activities. However, continued research and development are needed to ensure these technologies are accessible, affordable, and culturally sensitive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=autism%20spectrum%20disorder" title=" autism spectrum disorder"> autism spectrum disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=non-verbal%20communication" title=" non-verbal communication"> non-verbal communication</a>, <a href="https://publications.waset.org/abstracts/search?q=assistive%20technology" title=" assistive technology"> assistive technology</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/192336/the-application-of-ai-in-developing-assistive-technologies-for-non-verbal-individuals-with-autism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192336.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">19</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5197</span> Facial Recognition on the Basis of Facial Fragments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk">Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Bonilla%20Meza"> Sandra Bonilla Meza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild<em>) </em>face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=labeled%20faces%20in%20the%20wild%20%28LFW%29%20database" title=" labeled faces in the wild (LFW) database"> labeled faces in the wild (LFW) database</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20local%20descriptor%20%28RLD%29" title=" random local descriptor (RLD)"> random local descriptor (RLD)</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20features" title=" random features"> random features</a> </p> <a href="https://publications.waset.org/abstracts/50117/facial-recognition-on-the-basis-of-facial-fragments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50117.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5196</span> Using Optical Character Recognition to Manage the Unstructured Disaster Data into Smart Disaster Management System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dong%20Seop%20Lee">Dong Seop Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Sik%20Kim"> Byung Sik Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disaster%20information%20management" title="disaster information management">disaster information management</a>, <a href="https://publications.waset.org/abstracts/search?q=unstructured%20data" title=" unstructured data"> unstructured data</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20character%20recognition" title=" optical character recognition"> optical character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning "> machine learning </a> </p> <a href="https://publications.waset.org/abstracts/113914/using-optical-character-recognition-to-manage-the-unstructured-disaster-data-into-smart-disaster-management-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5195</span> The Artificial Intelligence Technologies Used in PhotoMath Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tala%20Toonsi">Tala Toonsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marah%20Alagha"> Marah Alagha</a>, <a href="https://publications.waset.org/abstracts/search?q=Lina%20Alnowaiser"> Lina Alnowaiser</a>, <a href="https://publications.waset.org/abstracts/search?q=Hala%20Rajab"> Hala Rajab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photomath" title="photomath">photomath</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=app" title=" app"> app</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20equations." title=" mathematical equations."> mathematical equations.</a> </p> <a href="https://publications.waset.org/abstracts/145072/the-artificial-intelligence-technologies-used-in-photomath-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145072.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5194</span> DBN-Based Face Recognition System Using Light Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bing%20Gu">Bing Gu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20field" title=" light field"> light field</a>, <a href="https://publications.waset.org/abstracts/search?q=Lytro" title=" Lytro"> Lytro</a> </p> <a href="https://publications.waset.org/abstracts/10821/dbn-based-face-recognition-system-using-light-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5193</span> Development of a Sequential Multimodal Biometric System for Web-Based Physical Access Control into a Security Safe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babatunde%20Olumide%20Olawale">Babatunde Olumide Olawale</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyebode%20Olumide%20Oyediran"> Oyebode Olumide Oyediran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=access%20control" title="access control">access control</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20biometrics" title=" multimodal biometrics"> multimodal biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20safe" title=" security safe"> security safe</a> </p> <a href="https://publications.waset.org/abstracts/73150/development-of-a-sequential-multimodal-biometric-system-for-web-based-physical-access-control-into-a-security-safe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5192</span> The Capacity of Mel Frequency Cepstral Coefficients for Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20S.%20Al-Anzi">Fawaz S. Al-Anzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dia%20AbuZeina"> Dia AbuZeina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20features" title=" acoustic features"> acoustic features</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency" title=" mel frequency"> mel frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients" title=" cepstral coefficients"> cepstral coefficients</a> </p> <a href="https://publications.waset.org/abstracts/78382/the-capacity-of-mel-frequency-cepstral-coefficients-for-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">259</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5191</span> Face Tracking and Recognition Using Deep Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Degale%20Desta">Degale Desta</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jian"> Cheng Jian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-RCNN" title=" fast-RCNN"> fast-RCNN</a> </p> <a href="https://publications.waset.org/abstracts/163134/face-tracking-and-recognition-using-deep-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163134.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5190</span> The Face Sync-Smart Attendance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bekkem%20Chakradhar%20Reddy">Bekkem Chakradhar Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Soni%20Priya"> Y. Soni Priya</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathivanan%20G."> Mathivanan G.</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20K.%20Joshila%20Grace"> L. K. Joshila Grace</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Srinivasan"> N. Srinivasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Asha%20P."> Asha P.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=python" title="python">python</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CV2" title=" CV2"> CV2</a>, <a href="https://publications.waset.org/abstracts/search?q=smtplib" title=" smtplib"> smtplib</a>, <a href="https://publications.waset.org/abstracts/search?q=Dlib." title=" Dlib."> Dlib.</a> </p> <a href="https://publications.waset.org/abstracts/184212/the-face-sync-smart-attendance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">58</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5189</span> Investigating the Effect of Mobile Technologies Dimensions upon Creativity of Kermanshah Polymer Petrochemical Company’s Employees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghafor%20Ahmadi">Ghafor Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nader%20Bohloli%20Zynab"> Nader Bohloli Zynab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid scientific changes are the driving force of upheaval. As new technologies arrive, human’s life changes and information becomes one of the productive sources besides other factors. Optimum application of each technology depends on precise recognition of that technology. Options of mobile phones are constantly developing and evolving. Meanwhile, one of the influential variables for improving the performance and eternity of organizations is creativity. One of the new technologies tied with development and innovation is mobile phone. In this research, the contribution of different dimensions of mobile technologies such as perceived use, perceived enjoyment, continuance intention, confirmation and satisfaction to creativity of employees were investigated. Statistical population included 510 employees of Kermanshah Petrochemical Company. Sample size was defined 217 based on Morgan and Krejcie table. This study is descriptive and data gathering instrument was a questionnaire. Applying SPSS software, linear regression was analyzed. It was found out that all dimensions of mobile technologies except satisfaction affect on creativity of employees. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20technologies" title="mobile technologies">mobile technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=continuance%20intention" title=" continuance intention"> continuance intention</a>, <a href="https://publications.waset.org/abstracts/search?q=perceived%20enjoyment" title=" perceived enjoyment"> perceived enjoyment</a>, <a href="https://publications.waset.org/abstracts/search?q=perceived%20use" title=" perceived use"> perceived use</a>, <a href="https://publications.waset.org/abstracts/search?q=confirmation" title=" confirmation"> confirmation</a>, <a href="https://publications.waset.org/abstracts/search?q=satisfaction" title=" satisfaction"> satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=creativity" title=" creativity"> creativity</a> </p> <a href="https://publications.waset.org/abstracts/59164/investigating-the-effect-of-mobile-technologies-dimensions-upon-creativity-of-kermanshah-polymer-petrochemical-companys-employees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59164.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5188</span> Pre-Analysis of Printed Circuit Boards Based on Multispectral Imaging for Vision Based Recognition of Electronics Waste</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Florian%20Kleber">Florian Kleber</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Kampel"> Martin Kampel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand of gallium, indium and rare-earth elements for the production of electronics, e.g. solid state-lighting, photovoltaics, integrated circuits, and liquid crystal displays, will exceed the world-wide supply according to current forecasts. Recycling systems to reclaim these materials are not yet in place, which challenges the sustainability of these technologies. This paper proposes a multispectral imaging system as a basis for a vision based recognition system for valuable components of electronics waste. Multispectral images intend to enhance the contrast of images of printed circuit boards (single components, as well as labels) for further analysis, such as optical character recognition and entire printed circuit board recognition. The results show that a higher contrast is achieved in the near infrared compared to ultraviolet and visible light. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronics%20waste" title="electronics waste">electronics waste</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20imaging" title=" multispectral imaging"> multispectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=printed%20circuit%20boards" title=" printed circuit boards"> printed circuit boards</a>, <a href="https://publications.waset.org/abstracts/search?q=rare-earth%20elements" title=" rare-earth elements"> rare-earth elements</a> </p> <a href="https://publications.waset.org/abstracts/15815/pre-analysis-of-printed-circuit-boards-based-on-multispectral-imaging-for-vision-based-recognition-of-electronics-waste" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15815.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">415</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5187</span> Comparing Emotion Recognition from Voice and Facial Data Using Time Invariant Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vesna%20Kirandziska">Vesna Kirandziska</a>, <a href="https://publications.waset.org/abstracts/search?q=Nevena%20Ackovska"> Nevena Ackovska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Madevska%20Bogdanova"> Ana Madevska Bogdanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/42384/comparing-emotion-recognition-from-voice-and-facial-data-using-time-invariant-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42384.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5186</span> Iris Feature Extraction and Recognition Based on Two-Dimensional Gabor Wavelength Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bamidele%20Samson%20Alobalorun">Bamidele Samson Alobalorun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ifedotun%20Roseline%20Idowu"> Ifedotun Roseline Idowu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daugman%20rubber%20sheet" title="Daugman rubber sheet">Daugman rubber sheet</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamming%20distance" title=" Hamming distance"> Hamming distance</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition%20system" title=" iris recognition system"> iris recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20Gabor%20wavelet%20transform" title=" 2D Gabor wavelet transform"> 2D Gabor wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/170345/iris-feature-extraction-and-recognition-based-on-two-dimensional-gabor-wavelength-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5185</span> A Communication Signal Recognition Algorithm Based on Holder Coefficient Characteristics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hui%20Zhang">Hui Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Tian"> Ye Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Ye"> Fang Ye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziming%20Guo"> Ziming Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20signal" title="communication signal">communication signal</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=Holder%20coefficient" title=" Holder coefficient"> Holder coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20cloud%20model" title=" improved cloud model"> improved cloud model</a> </p> <a href="https://publications.waset.org/abstracts/101463/a-communication-signal-recognition-algorithm-based-on-holder-coefficient-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101463.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5184</span> A Contribution to Human Activities Recognition Using Expert System Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/171721/a-contribution-to-human-activities-recognition-using-expert-system-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171721.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5183</span> Switching to the Latin Alphabet in Kazakhstan: A Brief Overview of Character Recognition Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ainagul%20Yermekova">Ainagul Yermekova</a>, <a href="https://publications.waset.org/abstracts/search?q=Liudmila%20Goncharenko"> Liudmila Goncharenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Baghirzade"> Ali Baghirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Sybachin"> Sergey Sybachin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20method" title=" template method"> template method</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20algorithm" title=" recognition algorithm"> recognition algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20method" title=" structured method"> structured method</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20method" title=" feature method"> feature method</a> </p> <a href="https://publications.waset.org/abstracts/138734/switching-to-the-latin-alphabet-in-kazakhstan-a-brief-overview-of-character-recognition-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5182</span> Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gheida%20J.%20Shahrour">Gheida J. Shahrour</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20J.%20Russell"> Martin J. Russell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=person%20recognition" title="person recognition">person recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20recognition" title=" topic recognition"> topic recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=culture%20recognition" title=" culture recognition"> culture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20body%20movement%20signals" title=" 3D body movement signals"> 3D body movement signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20compensation" title=" variability compensation"> variability compensation</a> </p> <a href="https://publications.waset.org/abstracts/19473/recognizing-an-individual-their-topic-of-conversation-and-cultural-background-from-3d-body-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5181</span> Analyzing the Use of Augmented Reality and Image Recognition in Cultural Education: Use Case of Sintra Palace Treasure Hunt Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marek%20Maruszczak">Marek Maruszczak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gamified applications have been used successfully in education for years. The rapid development of technologies such as augmented reality and image recognition increases their availability and reduces their prices. Thus, there is an increasing possibility and need for a wide use of such applications in education. The main purpose of this article is to present the effects of work on a mobile application with augmented reality, the aim of which is to motivate tourists to pay more attention to the attractions and increase the likelihood of moving from one attraction to the next while visiting the Palácio Nacional de Sintra in Portugal. Work on the application was carried out together with the employees of Parques de Sintra from 2019 to 2021. Their effect was the preparation of a mobile application using augmented reality and image recognition. The application was tested on the palace premises by both Parques de Sintra employees and tourists visiting Palácio Nacional de Sintra. The collected conclusions allowed for the formulation of good practices and guidelines that can be used when designing gamified apps for the purpose of cultural education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality" title="augmented reality">augmented reality</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20education" title=" cultural education"> cultural education</a>, <a href="https://publications.waset.org/abstracts/search?q=gamification" title=" gamification"> gamification</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20games" title=" mobile games"> mobile games</a> </p> <a href="https://publications.waset.org/abstracts/139205/analyzing-the-use-of-augmented-reality-and-image-recognition-in-cultural-education-use-case-of-sintra-palace-treasure-hunt-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139205.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5180</span> Human Activities Recognition Based on Expert System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/151943/human-activities-recognition-based-on-expert-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5179</span> Image Processing techniques for Surveillance in Outdoor Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayanth%20C.">Jayanth C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Anirudh%20Sai%20Yetikuri"> Anirudh Sai Yetikuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavitha%20S.%20N."> Kavitha S. N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20detection" title=" pose detection"> pose detection</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20plate%20extraction" title=" number plate extraction"> number plate extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20analysis" title=" real-time analysis"> real-time analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20management" title=" database management"> database management</a> </p> <a href="https://publications.waset.org/abstracts/191153/image-processing-techniques-for-surveillance-in-outdoor-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">26</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5178</span> Enhanced Face Recognition with Daisy Descriptors Using 1BT Based Registration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sevil%20Igit">Sevil Igit</a>, <a href="https://publications.waset.org/abstracts/search?q=Merve%20Meric"> Merve Meric</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarp%20Erturk"> Sarp Erturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20descriptor" title=" Daisy descriptor"> Daisy descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=One-Bit%20Transform" title=" One-Bit Transform"> One-Bit Transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20registration" title=" image registration"> image registration</a> </p> <a href="https://publications.waset.org/abstracts/12593/enhanced-face-recognition-with-daisy-descriptors-using-1bt-based-registration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=173">173</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=174">174</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recognition%20technologies&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>