An adaptive computational model for texture segmentation

Extensions to current models for texture segmentation are presented, in which the underlying detector (filter) mechanisms are allowed to adapt to the incoming signal in terms of their dynamical response range and associativities. This system converges on new texton (B. Julesz, 1981) profiles of minimal dimensionality that are used to classify texture regions by a minimum distance classifier in the texture feature space. The three processes of convolution, cooperativity, and classification are individually analyzed and compared with some observations from human texture discrimination experiments.<
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