Texture discrimination using the Wigner-Ville distribution

In this paper we present a very effective method for texture segmentation and analysis using a local spectral method. The idea 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. Performance of the method is evaluated in different application domains: fabric defective textures and medical images yielding excellent results over other conventional spatial or spectral methods.