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基于蚁群优化的图像边缘检测算法
引用本文:张健,何坤1b,郑秀清,周激流. 基于蚁群优化的图像边缘检测算法[J]. 计算机工程, 2011, 37(17): 191-193
作者姓名:张健  何坤1b  郑秀清  周激流
作者单位:1. 四川大学电子信息学院,成都610065;四川师范大学物理与电子工程学院,成都610066
2. 四川大学计算机学院,成都,610065
基金项目:国家自然科学基金资助项目(60971109)
摘    要:为提高图像边缘检测的精度与抗噪性能,提出一种基于蚁群优化的图像边缘检测算法.将图像像素梯度值和像素圆形邻域统计均值的相对差共同作为蚁群的启发信息,引导蚁群搜索图像边缘.实验结果表明,该算法能最大限度地保留边缘细节,并能抑制噪声和纹理,具有较好的鲁棒性.

关 键 词:边缘检测  蚁群优化  特征提取  梯度  统计均值
收稿时间:2011-03-14

Image Edge Detection Algorithm Based on Ant Colony Optimization
ZHANG Jian,HE Kun,ZHENG Xiu-qing,ZHOU Ji-liu. Image Edge Detection Algorithm Based on Ant Colony Optimization[J]. Computer Engineering, 2011, 37(17): 191-193
Authors:ZHANG Jian  HE Kun  ZHENG Xiu-qing  ZHOU Ji-liu
Affiliation:ZHANG Jian1a,2,HE Kun1b,ZHENG Xiu-qing1b,ZHOU Ji-liu1b(1a.School of Electronics and Information Engineering,1b.College of Computer Science,Sichuan University,Chengdu 610065,China,2.School of Physics and Electronics Engineering,Sichuan Normal University,Chengdu 610066,China)
Abstract:In order to improve the image edge detection accuracy and noise performance,this paper proposes an image edge detection algorithm based on ant colony optimization.The value of pixel gradient and the relative difference of statistical means of the pixel's circle neighborhood are combined to be the heuristic information which can guide ant's searching.Experimental results show that the edge detected by the proposed algorithm is robust to noise and texture,and contains most of the edge details.
Keywords:edge detection  ant colony optimization  feature extraction  gradient  statistical mean  
本文献已被 CNKI 维普 万方数据 等数据库收录!
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