Abstract
In this paper, we present a novel occlusion detection scheme for hidden Markov modeled shapes. First, hidden Markov model (HMM) is built using multiple examples of the shape. A reference path for the shape is built from the HMM, which is nothing but optimal path followed by the most likely example. The reference path stores temporal information about the entire shape, while the HMM only retains relationship between temporal information. For the shape of interest, its optimal path through HMM is calculated and warped to match the reference path using dynamic time warping (DTW). Occluded part of the shape is detected by identifying imbalance among various components of the matching cost. Detection results obtained for two shape data sets are presented for varying degrees of occlusion.
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