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

被引：15

作者：

Cristóbal, G

Hormigo, J

机构：

[1] CSIC, Inst Opt, Dept Imagenes Vis, E-28006 Madrid, Spain

[2] Univ Malaga, Dept Arquitectura Computad, E-29071 Malaga, Spain

来源：

PATTERN RECOGNITION LETTERS | 1999年 / 20卷 / 03期

关键词：

Wigner distribution; Hebbian learning; principal component analysis; texture segmentation;

D O I：

10.1016/S0167-8655(99)00002-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：337 / 345

页数：9

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