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April 30, Wednesday
12:00 – 13:00

Visual texture segregation is the perceptual phenomenon in which perceptually coherent regions can be discriminated solely on the basis of texture information (rather than by their color, luminance, etc…). A central notion in the study of texture segregation is the one of feature gradient (or feature contrast). Indeed, perceptual boundaries in texture stimuli occur where texture features (such as orientation) vary drastically within small spatial distances. However, recent work has shown that salient perceptual singularities occur in visual textures even in the absence of feature gradients. In particular, in smoothly varying orientation-defined textures (ODTs) these non-smooth percepts can be predicted from a differential measure involving two texture curvatures, one tangential and one normal (Ben-Shahar 2006). Based on this recent theory, in this work, we devise a biologically-plausible algorithm for detecting perceptual singularities in images of orientation-defined textures. The model uses a three-layer circuit in which both even-symmetric cells and odd-symmetric cells are used to compute all possible directional derivatives of the dominant orientation, from which the tangential and normal curvatures at each spatial position are selected using non linear shunting inhibition. We present result of this biologically plausible model on ODT images and discuss how this model may be extended to handling general textures and natural images.