Abstract
Hidden Markov models (HMMs) have become a standard tool for pattern recognition in computer vision. Although parameter and topology estimation have been studied, and still are, detailed analysis of how these estimated parameters contribute to HMM performance is rarely addressed. We develop tools for measuring such contributions and illustrate key issues in a representative task of gesture recognition - 3D motion recovery from 2D projections.
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