Part-based probabilistic point matching

Graham McNeill, S. Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.
Original languageEnglish
Title of host publicationPattern Recognition, 2006. ICPR 2006. 18th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages382-386
Number of pages5
Volume2
ISBN (Print)0-7695-2521-0
DOIs
Publication statusPublished - 2006

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