Texture consists of local variance of grey level or edge intensity values. We have recently presented a Generalized Symmetry operator, that captures local spatial relations of image patterns. We 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, we show that our algorithm favorably compares with other computational approaches (correlation coefficient > 0.09 between our model's performance and available human performance). Stressing the necessity of benchmarks for Computer Vision algorithms, we construct an exhaustive set of textures that could be used as experimental stimuli for both humans and machines, and demonstrate the performance of the algorithm on some of these artificial textures as well as on natural images.