Abstract
This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures. These signatures are high dimensional statistical distributions computed by extracting several features from the set of geodesic distance maps to each point. The resulting high dimensional distributions are matched to perform retrieval using a fast approximate Wasserstein metric. This allows to propose a unifying framework for the compact description of planar shapes and 3-D surfaces.
This work has been done with the support of the French “Agence Nationale de la Recherche” (ANR), under grant NatImages (ANR-08-EMER-009).
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Rabin, J., Peyré, G., Cohen, L.D. (2010). Geodesic Shape Retrieval via Optimal Mass Transport. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_56
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