A Probabilistic Approach to Robust Shape Matching and Part Decomposition
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Date
01/2006Author
McNeill, Graham
Vijayakumar, Sethu
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Abstract
We present a probabilistic approach to shape matching which is invariant to rotation,
translation and scaling. Shapes are represented by unlabeled point sets, so
discontinuous boundaries and non-boundary points do not pose a problem. Occlusions,
significant dissimilarities between shapes and image clutter are explained by
a ‘background model’ and hence, their impact on the overall match is limited. By
simultaneously learning a part decomposition of both shapes, we are able to successfully
match shapes that differ as a result of independent part transformations
– a form of variation common amongst real objects of the same class. The effectiveness
of the matching algorithm is demonstrated using the benchmark MPEG-7
data set and real images.