Abstract
This paper proposes a novel graph matching algorithm based on skeletons and applies it to shape recognition based on object silhouettes. The main idea is to match the critical points (junction points and end points) on skeleton graphs by comparing the geodesic paths between end points and junction points of the skeleton. Our method is motivated by the fact that junction points can carry information about the global structure of an object while paths between junction points and end points can represent specific geometric information of local parts. Our method yields the promising accuracy rates on two shape datasets in the presence of articulations, stretching, boundary deformations, part occlusion and rotation.
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Xu, Y., Wang, B., Liu, W., Bai, X. (2010). Skeleton Graph Matching Based on Critical Points Using Path Similarity. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_44
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DOI: https://doi.org/10.1007/978-3-642-12297-2_44
Publisher Name: Springer, Berlin, Heidelberg
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