Edge detection of infrared image with CNN_DGA algorithm

被引：19

作者：

Wang, Wei ^{[1
]}

Yang, Li-Jun ^{[1
]}

Xie, Yu-Ting ^{[1
]}

An, You-wei ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun CQUPT, Coll Elect Engn, Chongqing 400065, Peoples R China

来源：

OPTIK | 2014年 / 125卷 / 01期

关键词：

Infrared image process; Cellular neural network distributed genetic algorithm (CNN_DGA); Edge detection algorithm; CELLULAR NEURAL-NETWORKS;

D O I：

10.1016/j.ijleo.2013.07.049

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper, infrared image edge detection algorithm based on the combination of the cellular neural networks (CNN) and distributed genetic algorithm (DGA) is proposed. The CNN template is used to train the network with distributed genetic algorithm (CNN_DGA). The experimental results show: the edge of the infrared image was extracted accurately with CNN_DGA edge detection algorithm; furthermore, the noise of the infrared image was reduced greatly. Compared with the edge detection algorithms based on cellular neural networks with template trained by particle swarm optimization, parameters' search range and convergence speed are greatly improved. (C) 2013 Elsevier GmbH. All rights reserved.

引用

页码：464 / 467

页数：4

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