A linear space algorithm for computing maximal common subsequences
Communications of the ACM
HMM-Based Continuous Sign Language Recognition Using Stochastic Grammars
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Space and Time Optimal Parallel Sequence Alignments
IEEE Transactions on Parallel and Distributed Systems
Continuous realtime gesture following and recognition
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Human movement analysis for interactive dance
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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We present a hybrid classification method applicable to gesture recognition. The method combines elements of Hidden Markov Models (HMM) and various Dynamic Programming Alignment (DPA) methods, such as edit distance, sequence alignment, and dynamic time warping. As opposed to existing approaches which treat HMM and DPA as either competing or complementing methods, we provide a common framework which allows us to combine ideas from both HMM and DPA research. The combined approach takes on the robustness and effectiveness of HMMs and the simplicity of DPA approaches. We have implemented and successfully tested the proposed algorithm on various gesture data.