Non-local Active Contours
Cohen, Laurent D.; Peyré, Gabriel; Jung, Miyoun (2012), Non-local Active Contours, in Bruckstein, Alfred M.; Ter Haar Romeny, Bart M.; Bronstein, Alexander M.; Bronstein, Michael M., Scale Space and Variational Methods in Computer Vision. Third International Conference, SSVM 2011, Ein-Gedi, Israel, May 29 - June 2, 2011, Revised Selected Papers, Springer : Berlin, p. 255-266. http://dx.doi.org/10.1007/978-3-642-24785-9_22
TypeCommunication / Conférence
External document linkhttp://hal.archives-ouvertes.fr/hal-00591281/fr/
Conference titleSSVM'11: 3rd International Conference on Scale Space and Variational Methods in Computer Vision
Book titleScale Space and Variational Methods in Computer Vision. Third International Conference, SSVM 2011, Ein-Gedi, Israel, May 29 - June 2, 2011, Revised Selected Papers
Book authorBruckstein, Alfred M.; Ter Haar Romeny, Bart M.; Bronstein, Alexander M.; Bronstein, Michael M.
Series titleLecture Notes in Computer Science
Number of pages798
MetadataShow full item record
Abstract (EN)This article introduces a new image segmentation method that makes use of non-local comparisons between pairs of patches of features. A non-local energy is defined by summing the interactions between pairs of patches inside and outside the segmented domain. A maximum radius of interaction can be adapted to fit the amount of variation of the features inside and outside the region to segment. This non-local energy is minimized using a level set approach. The corresponding curve evolution defines a non-local active contour that converges to a local minimum of our energy. In contrast to previous segmentation methods, this approach only requires a local homogeneity of the features inside and outside the region to segment. This does not impose a global homogeneity as required by region-based segmentation methods. This comparison principle is also less sensitive to initialization than edge-based approaches. We instantiate this novel framework using patches of intensity or color values as well as Gabor features. This allows us to segment regions with smoothly varying intensity or colors as well as well as complicated textures with a spatially varying local orientation.
Subjects / KeywordsActive contours; segmentations; non-local
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