- 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.
- Links To Paper
- 1st Link
- Bibtex format
- @Misc{EDI-INF-RR-0613,
- author = {
Graham McNeill
and Sethu Vijayakumar
},
- title = {A Probabilistic Approach to Robust Shape Matching and Part Decomposition},
- year = 2006,
- month = {Jan},
- url = {http://www.ipab.inf.ed.ac.uk/slmc/SLMCpeople/McNeill_G_files/techRepPart.pdf},
- }
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