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Color Snakes

Sapiro, Guillermo



Abstract: A framework for object segmentation in vector-valued images is presented in this paper. The scheme proposed is based on geometric active contours moving towards the objects to be detected in the color or vector-valued image. Objects boundaries are then obtained as geodesics or minimal weighted distance curves in a Riemannian space. The metric in this space is given by a definition of edges in vector-valued images. The curve flow corresponding to the proposed active contours holds formal existence, uniqueness, stability, and correctness results. Based on an efficient numerical algorithm for curve evolution, we present a number of examples of object detection in real color and texture images. These examples show the algorithm capability to automatically handle changes in the deforming curve topology. We conclude the paper presenting an extension of the color active contours which leads to a possible image flow for vector-valued image segmentation. The algorithm is based on moving each one of the image level-sets according to the proposed color active contours. This extension also shows the relation of the color geodesic active contours with a number of partial-differential-equations based image processing algorithms as anisotropic diffusion and shock filters.

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