Texture segmentation and analysis using local spectral methods

Abstract

In this paper we present two new methods for texture segmentation and analysis using local spectral methods. The first approach to the problem is to use a modular pattern detection in textured images based on the use of a pseudo-wigner distribution (PWD) followed by a decorrelation procedure that consists of a principal component analyzer (for texture segmentation). The goal is to combine the advantages of a high spectral resolution of a joint representation given by the pseudo-Wigner distribution (PWD) with an effective adaptive principal component analysis. The second approach is based on a modular procedure that encompasses a region of interest extraction procedure followed by a log-prolate filtering scheme (for texture classification). Performance of both methods is evaluated in different application domains: fabric defective textures, epithelial cell cultures and a diatom's classification scenario yielding excellent results over other conventional spatial or spectral methods.