Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation

Abstract : This paper presents a shape representation and a variational framework for the construction of diffeomorphisms that establish "meaningful" correspondences between images, in that they preserve the local geometry of singularities such as region boundaries. At the same time, the shape representation allows enforcing shape information locally in determining such region boundaries. Our representation is based on a kernel descriptor that characterizes local shape. This shape descriptor is robust to noise and forms a scale-space in which an appropriate scale can be chosen depending on the size of features of interest in the scene. In order to preserve local shape during the matching procedure, we introduce a novel constraint to traditional energybased approaches to estimate diffeomorphic deformations, and enforce it in a variational framework.
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[Technical Report] UCLA CSD-TR 050044, 2005, pp.8
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Byung-Woo Hong, Emmanuel Prados, Luminita Vese, Stefano Soatto. Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation. [Technical Report] UCLA CSD-TR 050044, 2005, pp.8. 〈inria-00590199〉

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