Fuzzy image fusion based on modified Self-Generating Neural Network

被引：30

作者：

Jiang, Hong ^{[1
]}

Tian, Yufen ^{[1
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 07期

关键词：

Self-Generating Neural Network; Fuzzy logic; Optimization; Pruning; Fuzzy fusion;

D O I：

10.1016/j.eswa.2011.01.052

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new fusion algorithm for multi-sensor images based on Self-Generating Neural Network (SGNN) and fuzzy logic is proposed in this paper. This study is an extension of the work described in Qin and Bao (2005). First, the order and frequency modifications for the current McKusick and Langley (M-L) optimization are proposed; next, by combining optimization and pruning together, the Pruning-And-One-Optimization-Composite (PAOOC) processing method is raised; and finally, a modified fuzzy fusion scheme using improved SGNN is put forward. Experimental results demonstrate that the posed fuzzy fusion scheme outperforms region-based fusion using wavelet multi-resolution (MR) segmentation, and region-based fusion using tree-structure wavelet MR segmentation, both in visual effect and objective evaluation criteria. In the meantime, simulations also show the effectiveness of our modifications for the current optimization and pruning methods, visually and objectively. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：8515 / 8523

页数：9

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