Efficient decomposition methods of fuzzy relation and their application to image decomposition

被引：14

作者：

Nobuhara, H

Hirota, K

Sessa, S

Pedrycz, W

机构：

[1] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Midori Ku, Yokohama, Kanagawa 2268502, Japan

[2] Univ Naples Federico II, DICOMMA, I-80134 Naples, Italy

[3] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2G7, Canada

来源：

APPLIED SOFT COMPUTING | 2005年 / 5卷 / 04期

关键词：

fuzzy relation; optimization; gradient method; image decomposition;

D O I：

10.1016/j.asoc.2004.09.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Two optimizations for decomposition problem of fuzzy relation ( image) are proposed. The first optimization is a fast decomposition method of fuzzy relation based on the properties of max and min operations and the simultaneous updating of the prototype. The second optimization corresponds to an improvement of a cost function, in order to obtain a good quality of the solution of the decomposition problem. (C) 2004 Elsevier B. V. All rights reserved.

引用

页码：399 / 408

页数：10

共 17 条

[1] Median filter based on fuzzy rules and its application to image restoration [J].