On merging hidden Markov models with deformable templates
نویسندگان
چکیده
Hidden Markov modeling has proven extremely useful for statistical analysis of speech signals. There are, however, inherent problems in two dimensional extensions to HMM's, one of which is the exponential complexity associated with fully 2-D HMM's. In this paper, 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 Pimagejtemplate]. The template that maximizes this probability provides an optimal segmentation of the image. This segmentation capability will be demonstrated in facial analysis applications.
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تاریخ انتشار 1995