On Learning Texture Edge Detectors

Texture is an inherently non–local image property. All common texture descriptors, therefore, have a significant spatial support which renders classical edge detection schemes inadequate for the detection of texture boundaries. In this paper we propose a novel scheme to learn filters for texture edge detection. Textures are defined by the statistical distribution of Gabor filter responses. Optimality criteria for detection reliability and localization accuracy are suggested in the spirit of Canny’s edge detector. Texture edges are determined as zero crossings of the difference of the two a posteriori class distributions. An optimization algorithm is designed to determine the best filter kernel according to the underlying quality measure. The effectiveness of the approach is demonstrated on texture mondrians composed from the Brodatz album and a series of synthetic aperture radar (SAR) imagery. Moreover, we indicate how the proposed scheme can be combined with snake–type algorithms for prior–knowledge driven boundary refinement and interactive annotation.