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

Byung-Woo Hong 1 Emmanuel Prados 1, 2 Stefano Soatto 1 Luminita Vese 1
2 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
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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Byung-Woo Hong, Emmanuel Prados, Stefano Soatto, Luminita Vese. Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, Jun 2006, New York, United States. pp.833- 840, ⟨10.1109/CVPR.2006.277⟩. ⟨inria-00377421⟩



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