Skip to main content
Article thumbnail
Location of Repository

Nonlinear parametric Hidden Markov Models

By Andrew D. Wilson and Aaron F. Bobick


In previous work [4], 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.

Year: 1997
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.