Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1593
Title: Deformable probability maps: probabilistic shape and appearance-based object segmentation
Authors: Chatzis, Sotirios P. 
Tsechpenakis, Gavriil 
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences
Keywords: Deformable models;Graphical models;Segmentation
Issue Date: Aug-2011
Source: Computer vision and image understanding, 2011, vol. 115, no. 8, pp. 1157–1169
Volume: 115
Issue: 8
Start page: 1157
End page: 1169
Journal: Computer Vision and Image Understanding 
Abstract: We present the Deformable Probability Maps (DPMs) for object segmentation, which are graphical learning models incorporating properties of deformable models into discriminative classification. The DPM configuration is described by probabilistic energy functionals, which incorporate shape and appearance, and determine boundary smoothness, image features consistency, and topology with respect to the image salient edges. Similarly to deformable models, DPMs are dynamic, and their evolution is solved as a MAP inference problem. DPMs offer two major advantages: (i) they extend the Markovian property in the image domain to incorporate local shape constraints, similar to the known internal energy of deformable models, and therefore provide increased robustness in capturing objects with fuzzy boundaries; (ii) during their evolution, DPMs update the region statistics, and therefore they are robust to image feature variations. In our experiments we evaluate the DPMs' performance in a variety of images, while we compare them with existing deformable models and classification approaches on standard benchmark datasets
URI: https://hdl.handle.net/20.500.14279/1593
ISSN: 1090235X
DOI: 10.1016/j.cviu.2010.09.010
Rights: © Elsevier
Type: Article
Affiliation : Imperial College London 
Indiana University 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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