一种基于群智能算法的图像匹配算法

详细信息    查看全文 | 推荐本文 |

英文篇名：An Image Matching Algorithm Based on Swarm Intelligence Algorithm 作者：李桃 英文作者：Li Tao;College of Computer Science, China West Normal University; 关键词：图像匹配 ; 基本要素 ; 布谷鸟算法 ; 群智能算法 英文关键词：image matching;;basic elements;;cuckoo algorithms;;swarm intelligence algorithm 中文刊名：XXDL 英文刊名：China Computer & Communication 机构：西华师范大学计算机学院; 出版日期：2019-02-25 出版单位：信息与电脑(理论版) 年：2019 期：No.422 语种：中文; 页：XXDL201904024 页数：3 CN：04 ISSN：11-2697/TP 分类号：60-61+70

摘要

图像匹配是图像处理技术中的重要研究领域,也是图像融合的基础。现有的图像匹配方法存在匹配速度慢、匹配精度低等问题。为了提高匹配效率,笔者采用群智能算法作为搜索策略搜索最优参数,提出了一种基于群智能算法的图像匹配算法。基于此,介绍了图像匹配的三个基本要素、分类以及性能评价指标,阐述了布谷鸟算法,并利用布谷鸟算法改进了原有的匹配算法。实验结果证明,基于群智能算法的图像匹配算法合理有效。
        Image matching is an important research field in image processing technology and the basis of image fusion. The existing image matching methods have some problems, such as slow matching speed and low matching accuracy. In order to improve the matching efficiency, swarm intelligence algorithm is used as the search strategy to search the optimal parameters, and an image matching algorithm based on swarm intelligence algorithm is proposed. Based on this, three basic elements, classification and performance evaluation index of image matching are introduced, cuckoo algorithm is expounded, and cuckoo algorithm is used to improve the original matching algorithm. The experimental results show that the image matching algorithm based on swarm intelligence algorithm is reasonable and effective.

引文

[1]章毓晋.图像工程[M].北京:清华大学出版社,2000:85.
    [2]沈振康,孙仲康.数字图像处理及应用[M].北京:国防工业出版,1983:76.
    [3]孙远,周刚慧,赵立初,等.灰度图像匹配的快速算法[J].上海交通大学学报,2000,34(5):702-704.