Abstract / Description of output
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 language | English |
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Title of host publication | Pattern Recognition, 2006. ICPR 2006. 18th International Conference on |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 382-386 |
Number of pages | 5 |
Volume | 2 |
ISBN (Print) | 0-7695-2521-0 |
DOIs | |
Publication status | Published - 2006 |