Texture consists of local variance of gray level or edge intensity values. The authors have recently presented a generalized symmetry operator that captures local spatial relations of image patterns. They show that activity differences in the continuous intensity map produced by the local generalized symmetry operator can be efficiently used to detect texture boundaries. Using almost all available quantitative results of human performance in artificial texture discrimination, the authors show that the algorithm favorably compares with other computational approaches. Stressing the necessity of benchmarks for computer vision algorithms, the authors also discuss an exhaustive set of textures that could be used as experimental stimuli for both humans and machines. The performance of the algorithm is demonstrated on some of these artificial textures as well as on natural images.
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