Patch-Based texture edges and segmentation

Authors:
Lior Wolf;Xiaolei Huang;Ian Martin;Dimitris Metaxas
Affiliations:
Center for Biological and Computational Learning, The McGovern Institute for Brain Research and dept. of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA;Division of Computer and Information Sciences, Rutgers University, New Brunswick, NJ;Center for Biological and Computational Learning, The McGovern Institute for Brain Research and dept. of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA;Division of Computer and Information Sciences, Rutgers University, New Brunswick, NJ
Venue:
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Year:
2006

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Abstract

A novel technique for extracting texture edges is introduced. It is based on the combination of two ideas: the patch-based approach, and non-parametric tests of distributions. Our method can reliably detect texture edges using only local information. Therefore, it can be computed as a preprocessing step prior to segmentation, and can be very easily combined with parametric deformable models. These models furnish our system with smooth boundaries and globally salient structures.