Localized texture processing in vision: analysis and synthesis in the Gaborian space

Abstract

Recent studies of cortical simple cell function suggest that the primitives of image representation in vision have a wavelet form similar to Gabor elementary functions (EF's). It is shown that textures and fully-textured images can be practically decomposed into, and synthesized from, a finite set of EF's. Textured-images can be synthesized from a set of EF's using image coefficient library. Alternatively, texturing of contoured (cartoon-like) images is analogous to adding chromaticity information to contoured images. A method for texture discrimination and image segmentation using local features based on the Gabor approach is introduced. Features related to the EF's parameters provide efficient means for texture discrimination and classification. This method is invariant under rotation and translation. The performance of the classification appears to be robust with respect to noisy conditions. The results show an insensitivity of the discrimination to relatively high noise levels, comparable to the performances of the human observer.