On merging hidden Markov models with deformable templates

Authors:
R. R. Rao;R. M. Mersereau
Affiliations:
-;-
Venue:
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Year:
1995

Citing 0
Cited 2

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Abstract

Hidden Markov modeling has proven extremely useful for statistical analysis of speech signals. There are, however, inherent problems in two dimensional extensions to HMMs, one of which is the exponential complexity associated with fully 2-D HMMs. We propose a new 2-D HMM-like structure obtained by embedding states within regions of a deformable template structure. With this state-embedded deformable template (SEDT), each region of a deformable template has an underlying observation probability distribution. This structure allows for computation of the P[image/template]. The template that maximizes this probability provides an optimal segmentation of the image. This segmentation capability is demonstrated in facial analysis applications.