TR#424: Nonlinear Parametric Hidden Markov Models
Andrew D. Wilson and Aaron F. Bobick
In previous work (see TR-421), we extended the hidden
Markov model (HMM) framework to incorporate a global parametric
variation in the output probabilities of the states of the HMM.
Development of the parametric HMM was motivated by the task of
simultaneoiusly recognizing and interpreting gestures that exhibit
meaningful variation. With standard HMMs, such global variation
confounds the recognition process. In this paper we extend the
parametric HMM approach to handle nonlinear (non-analytic)
dependencies of the output distributions on the parameter of interest.
We show a generalized expectation-maximization (GEM) algorithm for
training the parametric HMM and a GEM algorithm to simultaneously
recognize the gesture and estimate the value of the parameter. We
present results on a pointing gesture, where the nonlinear approach
permits the natural azimuth/elevation parameterization of pointing
direction.
Postscript .
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