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A Framework for Sign Language Recognition: Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition - Softcover
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This book describes new techniques that can be used in a sign language recognition (SLR) system, and more generally in human gesture systems. Any SLR system consists of three main components: Skin detector, Tracker, and Recognizer. The skin detector is responsible for segmenting skin objects like the face and hands from video frames. The tracker keeps track of the hand location (specifically the bounding box) and detects any occlusions that might happen between any skin objects. Finally, the recognizer tries to classify the performed sign into one of the sign classes in our vocabulary using the set of features and information provided by the tracker. Instead of dealing with the whole sign for recognition, the sign can be broken down into elementary subunits, which are far less in number than the total number of signs in the vocabulary. We propose a novel algorithm to model and segment these subunits, then try to learn the informative combinations of subunits/features using a boosting framework. In brief, This book takes you into a journey and describes all the necessary steps that are needed to recognize the meaning of a performed signs in a video.
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A Framework for Sign Language Recognition: Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition
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A Framework for Sign Language Recognition: Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition
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VDM Verlag Dr. M�ller 2010-05-04, 2010
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ISBN 13: 9783639126587
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A Framework for Sign Language Recognition Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Subunits for Recognition
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A Framework for Sign Language Recognition Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Subunits for Recognition
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A Framework for Sign Language Recognition
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VDM Verlag Dr. Müller, 2010
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ISBN 13: 9783639126587
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Awad GeorgeDr. George Awad received his B.sc. in Computer Engineering from the AASTMT, Egypt in 2000 and Msc. in 2004. He worked as a lecturer assistant till 2004. He received his Ph.D. from DCU, Ireland in 2007. Currently, he wor. Seller Inventory # 4959848
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A Framework for Sign Language Recognition : Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book describes new techniques that can be used in a sign language recognition (SLR) system, and more generally in human gesture systems. Any SLR system consists of three main components: Skin detector, Tracker, and Recognizer. The skin detector is responsible for segmenting skin objects like the face and hands from video frames. The tracker keeps track of the hand location (specifically the bounding box) and detects any occlusions that might happen between any skin objects. Finally, the recognizer tries to classify the performed sign into one of the sign classes in our vocabulary using the set of features and information provided by the tracker. Instead of dealing with the whole sign for recognition, the sign can be broken down into elementary subunits, which are far less in number than the total number of signs in the vocabulary. We propose a novel algorithm to model and segment these subunits, then try to learn the informative combinations of subunits/features using a boosting framework. In brief, This book takes you into a journey and describes all the necessary steps that are needed to recognize the meaning of a performed signs in a video. Seller Inventory # 9783639126587
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A Framework for Sign Language Recognition: Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition
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