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Prediction-Based Gesture Detection in Lecture Videos by Combining Visual, Speech and Electronic Slides
Toronto, ON, Canada July 09-July 12
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICME.2006.2625302006 IEEE International Conference on ...
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Feng Wang, Dept. of Computer Science, Hong Kong University of Science and Technology
Chong-wah Ngo, Dept. of Computer Science, City University of Hong Kong
Ting-chuen Pong, Dept. of Computer Science, Hong Kong University of Science and Technology
This paper presents an efficient algorithm for gesture detection in lecture videos by combining visual, speech and electronic slides. Besides accuracy, response time is also considered to cope with the efficiency requirements of real-time applications. Candidate gestures are first detected by visual cue. Then we modifity HMM models for complete gestures to predict and recognize incomplete gestures before the whole gestures paths are observed. Gesture recognition is used to verify the results of gesture detection. The relations between visual, speech and slides are analyzed. The correspondence between speech and gesture is employed to improve the accuracy and the responsiveness of gesture detection.
Citation:
Feng Wang, Chong-wah Ngo, Ting-chuen Pong, "Prediction-Based Gesture Detection in Lecture Videos by Combining Visual, Speech and Electronic Slides," icme, pp.653-656, 2006 IEEE International Conference on Multimedia and Expo, 2006
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