A Probabilistic Approach to Robust Shape Matching

We present a probabilistic approach to shape matching that is invariant to rotation, translation and scaling. Shapes can be represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusion, significant dissimilarities between shapes and image clutter are explained by a background model, and hence, their impact on the overall match is limited. The ability to operate on incomplete shape representations and ignore part of the input means that, unlike many matching algorithms, our technique performs well on real images. We derive a continuous version of the model which can be used when the ´query shape´ is more accurately described by a set of line segments-e.g. a boundary polygon or line drawing. The effectiveness of the algorithms is demonstrated using the benchmark MPEG-7 data set and real images.