Hidden Markov models for gesture recognition
作者： Donald O. Tanguay
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摘要: Understanding human motions can be posed as a pattern recognition problem. Humans express time-varying motion patterns (gestures), such wave, in order to convey message recipient. If computer detect and distinguish these patterns, the desired reconstructed, respond appropriately. This thesis describes an approach recognize domain-dependent gestures using statistical tool, Hidden Markov Model (HMM). Through several experiments with two-dimensional mouse gestures, this analyzes behavior of HMM training reports some important insights towards better performance. Thesis Supervisor: Aaron F. Bobick Title: Assistant Professor Computational Vision
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