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Titlebook: Advances in Visual Computing; 6th International Sy George Bebis,Richard Boyle,Lisa Avila Conference proceedings 2010 Springer Berlin Heidel

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期刊全稱Advances in Visual Computing
期刊簡稱6th International Sy
影響因子2023George Bebis,Richard Boyle,Lisa Avila
視頻videohttp://file.papertrans.cn/151/150111/150111.mp4
發(fā)行地址Fast conference proceedings.Up-to-date results.State-of-the-art report
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Visual Computing; 6th International Sy George Bebis,Richard Boyle,Lisa Avila Conference proceedings 2010 Springer Berlin Heidel
影響因子It is with great pleasure that we present the proceedings of the 6th Inter- tional, Symposium on Visual Computing (ISVC 2010), which was held in Las Vegas, Nevada. ISVC provides a common umbrella for the four main areas of visual computing including vision, graphics, visualization, and virtual reality. The goal is to provide a forum for researchers, scientists, engineers, and pr- titioners throughout the world to present their latest research ?ndings, ideas, developments, and applications in the broader area of visual computing. This year, the program consisted of 14 oral sessions, one poster session, 7 special tracks, and 6 keynote presentations. The response to the call for papers was very good; we received over 300 submissions for the main symposium from which we accepted 93 papers for oral presentation and 73 papers for poster p- sentation. Special track papers were solicited separately through the Organizing and Program Committees of each track. A total of 44 papers were accepted for oral presentation and 6 papers for poster presentation in the special tracks.
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https://doi.org/10.1007/978-981-16-5391-9overall behaviors of crowded scenes. Our experiments show promising results with 95.6% recognition rate for both the normal and abnormal behavior in coherent and incoherent crowded scenes. Additionally, the similar method is tested using flow feature vectors without incorporating social entropy for
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Incorporating Social Entropy for Crowd Behavior Detection Using SVMoverall behaviors of crowded scenes. Our experiments show promising results with 95.6% recognition rate for both the normal and abnormal behavior in coherent and incoherent crowded scenes. Additionally, the similar method is tested using flow feature vectors without incorporating social entropy for
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Consensus, Negotiation and Mediationhigh-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibility of the low-level image analysis algorithms.
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