Article ID Journal Published Year Pages File Type
531122 Pattern Recognition 2010 11 Pages PDF
Abstract

We propose a new algorithm for simultaneous localization and figure-ground segmentation where coupled region-edge shape priors are involved with two different but complementary roles. We resort to a segmentation-based hypothesis-and-test paradigm in this research, where the region prior is used to form a segmentation and the edge prior is used to evaluate the validity of the formed segmentation. Our fundamental assumption is that the optimal shape-constrained segmentation that maximizes the agreement with the edge prior occurs at the correctly hypothesized location. Essentially, the proposed algorithm addresses a mid-level vision issue that aims at producing a map image for part detection useful for high-level vision tasks. Our experiments demonstrated that this algorithm offers promising results in terms of both localization and segmentation.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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