Texture segmentation through eigen-analysis of the Pseudo-Wigner distribution

Abstract

In this paper we propose a new method for texture segmentation based on the use of texture feature detectors derived from a decorrelation procedure of a modified version of a Pseudo-Wigner distribution (PWD). The decorrelation procedure is accomplished by a cascade recursive least squared (CRLS) principal component (PC) neural network. The goal is to obtain a more efficient analysis of images by combining the advantages of using a high-resolution joint representation given by the PWD with an effective adaptive principal component analysis (PCA) through the use of feedforward neural networks.