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http://dx.doi.org/10.5391/IJFIS.2004.4.2.236

Recognition of 3D hand gestures using partially tuned composite hidden Markov models  

Kim, In Cheol (Communications Engineering Branch, Lister Hill National Center for Biomedical Communications National Library of Medicine)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.4, no.2, 2004 , pp. 236-240 More about this Journal
Abstract
Stroke-based composite HMMs with articulation states are proposed to deal with 3D spatio-temporal trajectory gestures. The direct use of 3D data provides more naturalness in generating gestures, thereby avoiding some of the constraints usually imposed to prevent performance degradation when trajectory data are projected into a specific 2D plane. Also, the decomposition of gestures into more primitive strokes is quite attractive, since reversely concatenating stroke-based HMMs makes it possible to construct a new set of gesture HMMs without retraining their parameters. Any deterioration in performance arising from decomposition can be remedied by a partial tuning process for such composite HMMs.
Keywords
Hand gesture recognition; Stroke-based composite hidden Markov model; Partial tuning;
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