Abstract

Symmetry is one of the most prominent cues in visual perception as well as in computer vision. We have recently presented the Generalized Symmetry Transform that receives as input an edge map, and outputs a symmetry map, where every point mark the intensity and orientation of the local generalized symmetry. In the context of Computer Vision, this map emphasizes points of high symmetry, which, in turn, are used to detect regions of interest for active vision systems. Many psychophysical experiments in texture discrimination use images that consist of various micro-patterns. Since the Generalize Symmetry Transform captures local spatial relations between image edges, we have used it to predict human performance in discrimination tasks. Applying the transform on micro-patterns of some well studied quantitative experiments of human texture discrimination, we show that symmetry, as characterized by our computational scheme, can account for most of them. Keywords: Generalized Symmetry, Textu...

Citations