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CVPR
2007
IEEE

Real-time Gesture Recognition with Minimal Training Requirements and On-line Learning

14 years 5 months ago
Real-time Gesture Recognition with Minimal Training Requirements and On-line Learning
In this paper, we introduce the semantic network model (SNM), a generalization of the hidden Markov model (HMM) that uses factorization of state transition probabilities to reduce training requirements, increase the efficiency of gesture recognition and on-line learning, and allow more precision in gesture modeling. We demonstrate the advantages both formally and experimentally, using examples such as full-body multimodal gesture recognition via optical motion capture and a pressure sensitive floor, as well as mouse / pen gesture recognition. Our results show that our algorithm performs much better than the traditional approach in situations where training samples are limited and/or the precision of the gesture model is high.
Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James
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