Components analysis of hidden Markov models in computer vision
نویسندگان
چکیده
Hidden Markov models (HMMs) have become a standard tool for pattern recognition in computer vision. However, issues of parameter estimation and evaluation are rarely addressed though they play key roles in just how HMMs perform. Without addressing these issues it can be readily shown that a so-called HMM model may actually be a Bayesian classifer or Markov Chain. In this paper we develop methods for addressing issues of assessing HMM component and parameter contributions and illustrate these issues in a representative task of gesture recognition 3D motion recovery from 2D projections.
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تاریخ انتشار 2003