TEXTURE SEGMENTATION USING PYRAMIDAL GABOR FUNCTIONS AND SELF-ORGANIZING FEATURE MAPS

被引：3

作者：

GUERINDUGUE, A ^{[1
]}

PALAGI, PM ^{[1
]}

机构：

[1] INST NATL POLYTECH GRENOBLE,TRAITEMENT IMAGES & RECONNAISSANCE FORMES LAB,F-38031 GRENOBLE,FRANCE

来源：

NEURAL PROCESSING LETTERS | 1994年 / 1卷 / 01期

关键词：

D O I：

10.1007/BF02312398

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents texture segmentation realised with image treatment methods and an artificial neural network model. Gabor oriented filters are used to extract frequential texture features and Self-Organising Feature Maps are used to group and interpolate these features. In order to decrease the number of filters, we use a pyramidal multiresolution method of image representation. We intend to build an architecture inspired by the early stages of the visual cortex, while making local frequential analysis of the images, which must be able to segment different textured images.

引用

页码：25 / 29

页数：5

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