báo cáo hóa học:" Research Article Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010 Article ID517861 13 pages doi 2010 517861 Research Article Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing Toshiyuki Kirishima 1 Yoshitsugu Manabe 1 Kosuke Sato 2 and Kunihiro Chihara1 1 Graduate School of Information Science Nara Institute of Science and Technology 8916-5 Takayama-cho Ikoma-shi Nara 630-0101 Japan 2 Graduate School of Engineering Science Osaka University 1-3 Machikaneyama-cho Toyonaka-shi Osaka 560-8531 Japan Correspondence should be addressed to Toshiyuki Kirishima kirishima@ Received 18 March 2009 Accepted 3 June 2009 Academic Editor Ling Shao Since a gesture involves a dynamic and complex motion multiview observation and recognition are desirable. For the better representation of gestures one needs to know in the first place from which views a gesture should be observed. Furthermore it becomes increasingly important how the recognition results are integrated when larger numbers of camera views are considered. To investigate these problems we propose a framework under which multiview recognition is carried out and an integration scheme by which the recognition results are integrated online and in realtime. For performance evaluation we use the ViHASi Virtual Human Action Silhouette public image database as a benchmark and our Japanese sign language JSL image database that contains 18 kinds of hand signs. By examining the recognition rates of each gesture for each view we found gestures that exhibit view dependency and the gestures that do not. Also we found that the view dependency itself could vary depending on the target gesture sets. By integrating the recognition results of different views our swarm-based integration provides more robust and better recognition performance than individual fixed-view recognition agents. Copyright 2010 Toshiyuki Kirishima et al. This is an open access article distributed

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