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descriptor</title> <meta name="description" content="Search results for: FST descriptor"> <meta name="keywords" content="FST descriptor"> <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 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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="FST descriptor"> <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> 59</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: FST descriptor</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Computing Some Topological Descriptors of Single-Walled Carbon Nanotubes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Bahrami">Amir Bahrami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the fields of chemical graph theory, molecular topology, and mathematical chemistry, a topological index or a descriptor index also known as a connectivity index is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are numerical parameters of a graph which characterize its topology and are usually graph invariant. Topological indices are used for example in the development of quantitative structure-activity relationships (QSARs) in which the biological activity or other properties of molecules are correlated with their chemical structure. In this paper some descriptor index (descriptor index) of single-walled carbon nanotubes, is determined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemical%20graph%20theory" title="chemical graph theory">chemical graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20topology" title=" molecular topology"> molecular topology</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptor" title=" molecular descriptor"> molecular descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=single-walled%20carbon%20nanotubes" title=" single-walled carbon nanotubes"> single-walled carbon nanotubes</a> </p> <a href="https://publications.waset.org/abstracts/39279/computing-some-topological-descriptors-of-single-walled-carbon-nanotubes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39279.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">338</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">58</span> Fused Structure and Texture (FST) Features for Improved Pedestrian Detection </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussin%20K.%20Ragb">Hussin K. Ragb</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayan%20K.%20Asari"> Vijayan K. Asari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title="pedestrian detection">pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20congruency" title=" phase congruency"> phase congruency</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase" title=" local phase"> local phase</a>, <a href="https://publications.waset.org/abstracts/search?q=LBP%20features" title=" LBP features"> LBP features</a>, <a href="https://publications.waset.org/abstracts/search?q=CSLBP%20features" title=" CSLBP features"> CSLBP features</a>, <a href="https://publications.waset.org/abstracts/search?q=FST%20descriptor" title=" FST descriptor"> FST descriptor</a> </p> <a href="https://publications.waset.org/abstracts/36643/fused-structure-and-texture-fst-features-for-improved-pedestrian-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36643.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">488</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">57</span> A New 3D Shape Descriptor Based on Multi-Resolution and Multi-Block CS-LBP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nihad%20Karim%20Chowdhury">Nihad Karim Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sanaullah%20Chowdhury"> Mohammad Sanaullah Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammed%20Jamshed%20Alam%20Patwary"> Muhammed Jamshed Alam Patwary</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubel%20Biswas"> Rubel Biswas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20shape%20retrieval" title="3D shape retrieval">3D shape retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20shape%20descriptor" title=" 3D shape descriptor"> 3D shape descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=CS-LBP" title=" CS-LBP"> CS-LBP</a>, <a href="https://publications.waset.org/abstracts/search?q=overlapped%20sub-images" title=" overlapped sub-images"> overlapped sub-images</a> </p> <a href="https://publications.waset.org/abstracts/40165/a-new-3d-shape-descriptor-based-on-multi-resolution-and-multi-block-cs-lbp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40165.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">445</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">56</span> 3D Objects Indexing Using Spherical Harmonic for Optimum Measurement Similarity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Hellam">S. Hellam</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Oulahrir"> Y. Oulahrir</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20El%20Mounchid"> F. El Mounchid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sadiq"> A. Sadiq</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mbarki"> S. Mbarki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a method for three-dimensional (3-D)-model indexing based on defining a new descriptor, which we call new descriptor using spherical harmonics. The purpose of the method is to minimize, the processing time on the database of objects models and the searching time of similar objects to request object. Firstly we start by defining the new descriptor using a new division of 3-D object in a sphere. Then we define a new distance which will be used in the search for similar objects in the database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20indexation" title="3D indexation">3D indexation</a>, <a href="https://publications.waset.org/abstracts/search?q=spherical%20harmonic" title=" spherical harmonic"> spherical harmonic</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20of%203D%20objects" title=" similarity of 3D objects"> similarity of 3D objects</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20similarity" title=" measurement similarity"> measurement similarity</a> </p> <a href="https://publications.waset.org/abstracts/14277/3d-objects-indexing-using-spherical-harmonic-for-optimum-measurement-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14277.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">433</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">55</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">54</span> Diversity Indices as a Tool for Evaluating Quality of Water Ways</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadra%20Ahmed">Khadra Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Kheireldin"> Khaled Kheireldin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=planktons" title="planktons">planktons</a>, <a href="https://publications.waset.org/abstracts/search?q=diversity%20indices" title=" diversity indices"> diversity indices</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20index" title=" water quality index"> water quality index</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20ways" title=" water ways"> water ways</a> </p> <a href="https://publications.waset.org/abstracts/36684/diversity-indices-as-a-tool-for-evaluating-quality-of-water-ways" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36684.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">518</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">53</span> Efficient Model Order Reduction of Descriptor Systems Using Iterative Rational Krylov Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Anwar">Muhammad Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameen%20Ullah"> Ameen Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Intakhab%20Alam%20Qadri"> Intakhab Alam Qadri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a technique utilizing the Iterative Rational Krylov Algorithm (IRKA) to reduce the order of large-scale descriptor systems. Descriptor systems, which incorporate differential and algebraic components, pose unique challenges in Model Order Reduction (MOR). The proposed method partitions the descriptor system into polynomial and strictly proper parts to minimize approximation errors, applying IRKA exclusively to the strictly adequate component. This approach circumvents the unbounded errors that arise when IRKA is directly applied to the entire system. A comparative analysis demonstrates the high accuracy of the reduced model and a significant reduction in computational burden. The reduced model enables more efficient simulations and streamlined controller designs. The study highlights IRKA-based MOR’s effectiveness in optimizing complex systems’ performance across various engineering applications. The proposed methodology offers a promising solution for reducing the complexity of large-scale descriptor systems while maintaining their essential characteristics and facilitating their analysis, simulation, and control design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20order%20reduction" title="model order reduction">model order reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptor%20systems" title=" descriptor systems"> descriptor systems</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20rational%20Krylov%20algorithm" title=" iterative rational Krylov algorithm"> iterative rational Krylov algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=interpolatory%20model%20reduction" title=" interpolatory model reduction"> interpolatory model reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20efficiency" title=" computational efficiency"> computational efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20methods" title=" projection methods"> projection methods</a>, <a href="https://publications.waset.org/abstracts/search?q=H%E2%82%82-optimal%20model%20reduction" title=" H₂-optimal model reduction"> H₂-optimal model reduction</a> </p> <a href="https://publications.waset.org/abstracts/189198/efficient-model-order-reduction-of-descriptor-systems-using-iterative-rational-krylov-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189198.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">31</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">52</span> A Biologically Inspired Approach to Automatic Classification of Textile Fabric Prints Based On Both Texture and Colour Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babar%20Khan">Babar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhijie"> Wang Zhijie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20classification" title="automatic classification">automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=colour%20descriptor" title=" colour descriptor"> colour descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=opponent%20colour%20channel" title=" opponent colour channel"> opponent colour channel</a> </p> <a href="https://publications.waset.org/abstracts/31715/a-biologically-inspired-approach-to-automatic-classification-of-textile-fabric-prints-based-on-both-texture-and-colour-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31715.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">484</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">51</span> Global Based Histogram for 3D Object Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somar%20Boubou">Somar Boubou</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatsuo%20Narikiyo"> Tatsuo Narikiyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Michihiro%20Kawanishi"> Michihiro Kawanishi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vision%20in%20control" title="vision in control">vision in control</a>, <a href="https://publications.waset.org/abstracts/search?q=robotics" title=" robotics"> robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20histogram%20of%20normal%20vectors" title=" differential histogram of normal vectors"> differential histogram of normal vectors</a> </p> <a href="https://publications.waset.org/abstracts/47486/global-based-histogram-for-3d-object-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47486.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">279</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">50</span> Enhancement Dynamic Cars Detection Based on Optimized HOG Descriptor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansouri%20Nabila">Mansouri Nabila</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Jemaa%20Yousra"> Ben Jemaa Yousra</a>, <a href="https://publications.waset.org/abstracts/search?q=Motamed%20Cina"> Motamed Cina</a>, <a href="https://publications.waset.org/abstracts/search?q=Watelain%20Eric"> Watelain Eric</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=car-detector" title="car-detector">car-detector</a>, <a href="https://publications.waset.org/abstracts/search?q=HOG" title=" HOG"> HOG</a>, <a href="https://publications.waset.org/abstracts/search?q=motion" title=" motion"> motion</a>, <a href="https://publications.waset.org/abstracts/search?q=computing%20time" title=" computing time"> computing time</a> </p> <a href="https://publications.waset.org/abstracts/40704/enhancement-dynamic-cars-detection-based-on-optimized-hog-descriptor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40704.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">323</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">49</span> Model Order Reduction of Continuous LTI Large Descriptor System Using LRCF-ADI and Square Root Balanced Truncation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sahadet%20Hossain">Mohammad Sahadet Hossain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamsil%20Arifeen"> Shamsil Arifeen</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrab%20Hossian%20Likhon"> Mehrab Hossian Likhon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze a linear time invariant (LTI) descriptor system of large dimension. Since these systems are difficult to simulate, compute and store, we attempt to reduce this large system using Low Rank Cholesky Factorized Alternating Directions Implicit (LRCF-ADI) iteration followed by Square Root Balanced Truncation. LRCF-ADI solves the dual Lyapunov equations of the large system and gives low-rank Cholesky factors of the gramians as the solution. Using these cholesky factors, we compute the Hankel singular values via singular value decomposition. Later, implementing square root balanced truncation, the reduced system is obtained. The bode plots of original and lower order systems are used to show that the magnitude and phase responses are same for both the systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low-rank%20cholesky%20factor%20alternating%20directions%20implicit%20iteration" title="low-rank cholesky factor alternating directions implicit iteration">low-rank cholesky factor alternating directions implicit iteration</a>, <a href="https://publications.waset.org/abstracts/search?q=LTI%20Descriptor%20system" title=" LTI Descriptor system"> LTI Descriptor system</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20equations" title=" Lyapunov equations"> Lyapunov equations</a>, <a href="https://publications.waset.org/abstracts/search?q=Square-root%20balanced%20truncation" title=" Square-root balanced truncation"> Square-root balanced truncation</a> </p> <a href="https://publications.waset.org/abstracts/26947/model-order-reduction-of-continuous-lti-large-descriptor-system-using-lrcf-adi-and-square-root-balanced-truncation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26947.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">418</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">48</span> Image Analysis for Obturator Foramen Based on Marker-controlled Watershed Segmentation and Zernike Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seda%20Sahin">Seda Sahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Emin%20Akata"> Emin Akata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20analysis" title="medical image analysis">medical image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20of%20bone%20structures%20on%20hip%20radiographs" title=" segmentation of bone structures on hip radiographs"> segmentation of bone structures on hip radiographs</a>, <a href="https://publications.waset.org/abstracts/search?q=marker-controlled%20watershed%20segmentation" title=" marker-controlled watershed segmentation"> marker-controlled watershed segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=zernike%20moment%20feature%20descriptor" title=" zernike moment feature descriptor"> zernike moment feature descriptor</a> </p> <a href="https://publications.waset.org/abstracts/31425/image-analysis-for-obturator-foramen-based-on-marker-controlled-watershed-segmentation-and-zernike-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31425.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">434</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">47</span> A Computer-Aided System for Tooth Shade Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zuhal%20Kurt">Zuhal Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Meral%20Kurt"> Meral Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Bilge%20T.%20Bal"> Bilge T. Bal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Ozkan"> Kemal Ozkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shade matching and reproduction is the most important element of success in prosthetic dentistry. Until recently, shade matching procedure was implemented by dentists visual perception with the help of shade guides. Since many factors influence visual perception; tooth shade matching using visual devices (shade guides) is highly subjective and inconsistent. Subjective nature of this process has lead to the development of instrumental devices. Nowadays, colorimeters, spectrophotometers, spectroradiometers and digital image analysing systems are used for instrumental shade selection. Instrumental devices have advantages that readings are quantifiable, can obtain more rapidly and simply, objectively and precisely. However, these devices have noticeable drawbacks. For example, translucent structure and irregular surfaces of teeth lead to defects on measurement with these devices. Also between the results acquired by devices with different measurement principles may make inconsistencies. So, its obligatory to search for new methods for dental shade matching process. A computer-aided system device; digital camera has developed rapidly upon today. Currently, advances in image processing and computing have resulted in the extensive use of digital cameras for color imaging. This procedure has a much cheaper process than the use of traditional contact-type color measurement devices. Digital cameras can be taken by the place of contact-type instruments for shade selection and overcome their disadvantages. Images taken from teeth show morphology and color texture of teeth. In last decades, a new method was recommended to compare the color of shade tabs taken by a digital camera using color features. This method showed that visual and computer-aided shade matching systems should be used as concatenated. Recently using methods of feature extraction techniques are based on shape description and not used color information. However, color is mostly experienced as an essential property in depicting and extracting features from objects in the world around us. When local feature descriptors with color information are extended by concatenating color descriptor with the shape descriptor, that descriptor will be effective on visual object recognition and classification task. Therefore, the color descriptor is to be used in combination with a shape descriptor it does not need to contain any spatial information, which leads us to use local histograms. This local color histogram method is remain reliable under variation of photometric changes, geometrical changes and variation of image quality. So, coloring local feature extraction methods are used to extract features, and also the Scale Invariant Feature Transform (SIFT) descriptor used to for shape description in the proposed method. After the combination of these descriptors, the state-of-art descriptor named by Color-SIFT will be used in this study. Finally, the image feature vectors obtained from quantization algorithm are fed to classifiers such as Nearest Neighbor (KNN), Naive Bayes or Support Vector Machines (SVM) to determine label(s) of the visual object category or matching. In this study, SVM are used as classifiers for color determination and shade matching. Finally, experimental results of this method will be compared with other recent studies. It is concluded from the study that the proposed method is remarkable development on computer aided tooth shade determination system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifiers" title="classifiers">classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20determination" title=" color determination"> color determination</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20system" title=" computer-aided system"> computer-aided system</a>, <a href="https://publications.waset.org/abstracts/search?q=tooth%20shade%20matching" title=" tooth shade matching"> tooth shade matching</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/51113/a-computer-aided-system-for-tooth-shade-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51113.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">444</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">46</span> Images Selection and Best Descriptor Combination for Multi-Shot Person Re-Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousra%20Hadj%20Hassen">Yousra Hadj Hassen</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Ayedi"> Walid Ayedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Ouni"> Tarek Ouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Jallouli"> Mohamed Jallouli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To re-identify a person is to check if he/she has been already seen over a cameras network. Recently, re-identifying people over large public cameras networks has become a crucial task of great importance to ensure public security. The vision community has deeply investigated this area of research. Most existing researches rely only on the spatial appearance information from either one or multiple person images. Actually, the real person re-id framework is a multi-shot scenario. However, to efficiently model a person’s appearance and to choose the best samples to remain a challenging problem. In this work, an extensive comparison of descriptors of state of the art associated with the proposed frame selection method is studied. Specifically, we evaluate the samples selection approach using multiple proposed descriptors. We show the effectiveness and advantages of the proposed method by extensive comparisons with related state-of-the-art approaches using two standard datasets PRID2011 and iLIDS-VID. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20network" title="camera network">camera network</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptor" title=" descriptor"> descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-shot" title=" multi-shot"> multi-shot</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=selection" title=" selection"> selection</a> </p> <a href="https://publications.waset.org/abstracts/65815/images-selection-and-best-descriptor-combination-for-multi-shot-person-re-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65815.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">278</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">45</span> QSAR Modeling of Germination Activity of a Series of 5-(4-Substituent-Phenoxy)-3-Methylfuran-2(5H)-One Derivatives with Potential of Strigolactone Mimics toward Striga hermonthica</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kova%C4%8Devi%C4%87">Strahinja Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovi%C4%87"> Sanja Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevri%C4%87"> Lidija Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Prandi"> Cristina Prandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Piermichele%20Kobauri"> Piermichele Kobauri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study is based on molecular modeling of a series of twelve 5-(4-substituent-phenoxy)-3-methylfuran-2(5H)-one derivatives which have potential of strigolactones mimics toward Striga hermonthica. The first step of the analysis included the calculation of molecular descriptors which numerically describe the structures of the analyzed compounds. The descriptors ALOGP (lipophilicity), AClogS (water solubility) and BBB (blood-brain barrier penetration), served as the input variables in multiple linear regression (MLR) modeling of germination activity toward S. hermonthica. Two MLR models were obtained. The first MLR model contains ALOGP and AClogS descriptors, while the second one is based on these two descriptors plus BBB descriptor. Despite the braking Topliss-Costello rule in the second MLR model, it has much better statistical and cross-validation characteristics than the first one. The ALOGP and AClogS descriptors are often very suitable predictors of the biological activity of many compounds. They are very important descriptors of the biological behavior and availability of a compound in any biological system (i.e. the ability to pass through the cell membranes). BBB descriptor defines the ability of a molecule to pass through the blood-brain barrier. Besides the lipophilicity of a compound, this descriptor carries the information of the molecular bulkiness (its value strongly depends on molecular bulkiness). According to the obtained results of MLR modeling, these three descriptors are considered as very good predictors of germination activity of the analyzed compounds toward S. hermonthica seeds. This article is based upon work from COST Action (FA1206), supported by COST (European Cooperation in Science and Technology). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemometrics" title="chemometrics">chemometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=germination%20activity" title=" germination activity"> germination activity</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20modeling" title=" molecular modeling"> molecular modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=QSAR%20analysis" title=" QSAR analysis"> QSAR analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=strigolactones" title=" strigolactones"> strigolactones</a> </p> <a href="https://publications.waset.org/abstracts/49457/qsar-modeling-of-germination-activity-of-a-series-of-5-4-substituent-phenoxy-3-methylfuran-25h-one-derivatives-with-potential-of-strigolactone-mimics-toward-striga-hermonthica" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49457.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">286</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">44</span> Emotion Recognition with Occlusions Based on Facial Expression Reconstruction and Weber Local Descriptor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jadisha%20Cornejo">Jadisha Cornejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Helio%20Pedrini"> Helio Pedrini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature. <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%20expression" title=" facial expression"> facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=occlusion" title=" occlusion"> occlusion</a>, <a href="https://publications.waset.org/abstracts/search?q=fiducial%20landmarks" title=" fiducial landmarks"> fiducial landmarks</a> </p> <a href="https://publications.waset.org/abstracts/90510/emotion-recognition-with-occlusions-based-on-facial-expression-reconstruction-and-weber-local-descriptor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90510.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">182</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">43</span> Drivers of Liking: Probiotic Petit Suisse Cheese</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helena%20Bolini">Helena Bolini</a>, <a href="https://publications.waset.org/abstracts/search?q=Erick%20Esmerino"> Erick Esmerino</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriano%20Cruz"> Adriano Cruz</a>, <a href="https://publications.waset.org/abstracts/search?q=Juliana%20Paixao"> Juliana Paixao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The currently concern for health has increased demand for low-calorie ingredients and functional foods as probiotics. Understand the reasons that infer on food choice, besides a challenging task, it is important step for development and/or reformulation of existing food products. The use of appropriate multivariate statistical techniques, such as External Preference Map (PrefMap), associated with regression by Partial Least Squares (PLS) can help in determining those factors. Thus, this study aimed to determine, through PLS regression analysis, the sensory attributes considered drivers of liking in probiotic petit suisse cheeses, strawberry flavor, sweetened with different sweeteners. Five samples in same equivalent sweetness: PROB1 (Sucralose 0.0243%), PROB2 (Stevia 0.1520%), PROB3 (Aspartame 0.0877%), PROB4 (Neotame 0.0025%) and PROB5 (Sucrose 15.2%) determined by just-about-right and magnitude estimation methods, and three commercial samples COM1, COM2 and COM3, were studied. Analysis was done over data coming from QDA, performed by 12 expert (highly trained assessors) on 20 descriptor terms, correlated with data from assessment of overall liking in acceptance test, carried out by 125 consumers, on all samples. Sequentially, results were submitted to PLS regression using XLSTAT software from Byossistemes. As shown in results, it was possible determine, that three sensory descriptor terms might be considered drivers of liking of probiotic petit suisse cheese samples added with sweeteners (p<0.05). The milk flavor was noticed as a sensory characteristic with positive impact on acceptance, while descriptors bitter taste and sweet aftertaste were perceived as descriptor terms with negative impact on acceptance of petit suisse probiotic cheeses. It was possible conclude that PLS regression analysis is a practical and useful tool in determining drivers of liking of probiotic petit suisse cheeses sweetened with artificial and natural sweeteners, allowing food industry to understand and improve their formulations maximizing the acceptability of their products. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acceptance" title="acceptance">acceptance</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer" title=" consumer"> consumer</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20descriptive%20analysis" title=" quantitative descriptive analysis"> quantitative descriptive analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=sweetener" title=" sweetener"> sweetener</a> </p> <a href="https://publications.waset.org/abstracts/23155/drivers-of-liking-probiotic-petit-suisse-cheese" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23155.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">446</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">42</span> Qsar Studies of Certain Novel Heterocycles Derived From bis-1, 2, 4 Triazoles as Anti-Tumor Agents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madhusudan%20Purohit">Madhusudan Purohit</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephen%20Philip"> Stephen Philip</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharathkumar%20Inturi"> Bharathkumar Inturi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20QSAR" title="3D QSAR">3D QSAR</a>, <a href="https://publications.waset.org/abstracts/search?q=CoMSIA" title=" CoMSIA"> CoMSIA</a>, <a href="https://publications.waset.org/abstracts/search?q=triazoles" title=" triazoles"> triazoles</a>, <a href="https://publications.waset.org/abstracts/search?q=novel%20heterocycles" title=" novel heterocycles"> novel heterocycles</a> </p> <a href="https://publications.waset.org/abstracts/3714/qsar-studies-of-certain-novel-heterocycles-derived-from-bis-1-2-4-triazoles-as-anti-tumor-agents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3714.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">444</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">41</span> An Efficient Motion Recognition System Based on LMA Technique and a Discrete Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Ajili">Insaf Ajili</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Mallem"> Malik Mallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Didier"> Jean-Yves Didier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition" title="human motion recognition">human motion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20representation" title=" motion representation"> motion representation</a>, <a href="https://publications.waset.org/abstracts/search?q=Laban%20Movement%20Analysis" title=" Laban Movement Analysis"> Laban Movement Analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Hidden%20Markov%20Model" title=" Discrete Hidden Markov Model"> Discrete Hidden Markov Model</a> </p> <a href="https://publications.waset.org/abstracts/87469/an-efficient-motion-recognition-system-based-on-lma-technique-and-a-discrete-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87469.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">207</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">40</span> A Semantic and Concise Structure to Represent Human Actions </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tobias%20Str%C3%BCbing">Tobias Strübing</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Ziaeetabar"> Fatemeh Ziaeetabar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enriched%20semantic%20event%20chain" title="enriched semantic event chain">enriched semantic event chain</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation" title=" semantic action representation"> semantic action representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20relations" title=" spatial relations"> spatial relations</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/129003/a-semantic-and-concise-structure-to-represent-human-actions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129003.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">126</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">39</span> Relevant LMA Features for Human Motion Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Ajili">Insaf Ajili</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Mallem"> Malik Mallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Didier"> Jean-Yves Didier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discriminative%20LMA%20features" title="discriminative LMA features">discriminative LMA features</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20reduction" title=" features reduction"> features reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition" title=" human motion recognition"> human motion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/96299/relevant-lma-features-for-human-motion-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96299.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">195</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">38</span> Improved Skin Detection Using Colour Space and Texture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Medjram%20Sofiane">Medjram Sofiane</a>, <a href="https://publications.waset.org/abstracts/search?q=Babahenini%20Mohamed%20Chaouki"> Babahenini Mohamed Chaouki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Benali%20Yamina"> Mohamed Benali Yamina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skin detection is an important task for computer vision systems. A good method for skin detection means a good and successful result of the system. The colour is a good descriptor that allows us to detect skin colour in the images, but because of lightings effects and objects that have a similar colour skin, skin detection becomes difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr colour skin model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=skin%20detection" title="skin detection">skin detection</a>, <a href="https://publications.waset.org/abstracts/search?q=YCbCr" title=" YCbCr"> YCbCr</a>, <a href="https://publications.waset.org/abstracts/search?q=GLCM" title=" GLCM"> GLCM</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20skin" title=" human skin"> human skin</a> </p> <a href="https://publications.waset.org/abstracts/19039/improved-skin-detection-using-colour-space-and-texture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19039.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">459</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">37</span> The Relationship Study between Topological Indices in Contrast with Thermodynamic Properties of Amino Acids</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Esmat%20Mohammadinasab">Esmat Mohammadinasab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Sadeghi"> Mostafa Sadeghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study are computed some thermodynamic properties such as entropy and specific heat capacity, enthalpy, entropy and gibbs free energy in 10 type different Aminoacids using Gaussian software with DFT method and 6-311G basis set. Then some topological indices such as Wiener, shultz are calculated for mentioned molecules. Finaly is showed relationship between thermodynamic peoperties and above topological indices and with different curves is represented that there is a good correlation between some of the quantum properties with topological indices of them. The instructive example is directed to the design of the structure-property model for predicting the thermodynamic properties of the amino acids which are discussed here. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amino%20acids" title="amino acids">amino acids</a>, <a href="https://publications.waset.org/abstracts/search?q=DFT%20Method" title=" DFT Method"> DFT Method</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptor" title=" molecular descriptor"> molecular descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=thermodynamic%20properties" title=" thermodynamic properties "> thermodynamic properties </a> </p> <a href="https://publications.waset.org/abstracts/23718/the-relationship-study-between-topological-indices-in-contrast-with-thermodynamic-properties-of-amino-acids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23718.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">432</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">36</span> A New Method Separating Relevant Features from Irrelevant Ones Using Fuzzy and OWA Operator Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imed%20Feki">Imed Feki</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Msahli"> Faouzi Msahli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20sensitivity" title="data sensitivity">data sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=OWA%20operators" title=" OWA operators"> OWA operators</a>, <a href="https://publications.waset.org/abstracts/search?q=percolation%20technique" title=" percolation technique"> percolation technique</a> </p> <a href="https://publications.waset.org/abstracts/27691/a-new-method-separating-relevant-features-from-irrelevant-ones-using-fuzzy-and-owa-operator-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27691.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">605</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">35</span> AS-Geo: Arbitrary-Sized Image Geolocalization with Learnable Geometric Enhancement Resizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huayuan%20Lu">Huayuan Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chunfang%20Yang"> Chunfang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma%20Zhu"> Ma Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Baojun%20Qi"> Baojun Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaqiong%20Qiao"> Yaqiong Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangqian%20Xu"> Jiangqian Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20geolocalization" title="image geolocalization">image geolocalization</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention%20mechanism" title=" self-attention mechanism"> self-attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20resizer" title=" image resizer"> image resizer</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20feature" title=" geometric feature"> geometric feature</a> </p> <a href="https://publications.waset.org/abstracts/152265/as-geo-arbitrary-sized-image-geolocalization-with-learnable-geometric-enhancement-resizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152265.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">214</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">34</span> Speeding-up Gray-Scale FIC by Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eman%20A.%20Al-Hilo">Eman A. Al-Hilo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawraa%20H.%20Al-Waelly"> Hawraa H. Al-Waelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20gray%20level%20image" title="fractal gray level image">fractal gray level image</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20compression%20technique" title=" fractal compression technique"> fractal compression technique</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20function%20system" title=" iterated function system"> iterated function system</a>, <a href="https://publications.waset.org/abstracts/search?q=moments%20feature" title=" moments feature"> moments feature</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-mean%20range-domain%20block" title=" zero-mean range-domain block"> zero-mean range-domain block</a> </p> <a href="https://publications.waset.org/abstracts/19903/speeding-up-gray-scale-fic-by-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19903.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">492</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">33</span> Hand Motion Trajectory Analysis for Dynamic Hand Gestures Used in Indian Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daleesha%20M.%20Viswanathan">Daleesha M. Viswanathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumam%20Mary%20Idicula"> Sumam Mary Idicula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=orientation%20features" title="orientation features">orientation features</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20feature%20vector" title=" discrete feature vector"> discrete feature vector</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM." title=" HMM."> HMM.</a>, <a href="https://publications.waset.org/abstracts/search?q=Indian%20sign%20language" title=" Indian sign language"> Indian sign language</a> </p> <a href="https://publications.waset.org/abstracts/35653/hand-motion-trajectory-analysis-for-dynamic-hand-gestures-used-in-indian-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35653.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">370</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">32</span> Bag of Words Representation Based on Weighting Useful Visual Words</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BOW" title="BOW">BOW</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20visual%20words" title=" useful visual words"> useful visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20visual%20words" title=" weighted visual words"> weighted visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title=" bag of visual words"> bag of visual words</a> </p> <a href="https://publications.waset.org/abstracts/14009/bag-of-words-representation-based-on-weighting-useful-visual-words" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14009.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">436</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">31</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">30</span> An Improvement of Multi-Label Image Classification Method Based on Histogram of Oriented Gradient</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ziad%20Abdallah">Ziad Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Oueidat"> Mohamad Oueidat</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20El-Zaart"> Ali El-Zaart</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval%20system" title=" information retrieval system"> information retrieval system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-label" title=" multi-label"> multi-label</a>, <a href="https://publications.waset.org/abstracts/search?q=problem%20transformation" title=" problem transformation"> problem transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20of%20gradients" title=" histogram of gradients"> histogram of gradients</a> </p> <a href="https://publications.waset.org/abstracts/66645/an-improvement-of-multi-label-image-classification-method-based-on-histogram-of-oriented-gradient" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66645.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">374</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=FST%20descriptor&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=FST%20descriptor&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> 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