2-D image matching based on Hausdorff distance and quantum particle swarm optimization

doi:10.3778/j.issn.1002-8331.2010.17.051

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (17): 179-181.DOI: 10.3778/j.issn.1002-8331.2010.17.051

• 图形、图像、模式识别 • Previous Articles Next Articles

2-D image matching based on Hausdorff distance and quantum particle swarm optimization

ZHANG Yi¹，PENG Xiao-ming¹，CHEN Wu-fan^1，2

1.College of Automation，University of Electronic Science and Technology of China，Chengdu 610054，China
2.Department of Biomedical Engineering，Southern Medical University，Guangzhou 510515，China

Received:2008-12-02 Revised:2009-02-27 Online:2010-06-11 Published:2010-06-11
Contact: ZHANG Yi

Hausdorff距离和量子粒子群的二维图像匹配

张毅¹，彭晓明¹，陈武凡^1，2

1.电子科技大学自动化工程学院，成都 610054
2.南方医科大学生物医学工程系，广州 510515

通讯作者: 张毅

Abstract

Abstract: A two-dimensional image matching method based on the Hausdorff distance and Quantum Particle Swarm Optimization（QPSO） is presented.First，edges are extracted from the original images by the Canny edge detector.Then，a similarity function based on the Hausdorff distance is constructed.Finally，the（QPSO） is adopted to optimize the similarity function.Experiments show that the proposed method is able to locate the object of interest globally and efficiently.

Key words: two-dimensional image-search, Hausdorff distance, quantum particle swarm optimization

摘要： 提出了一种基于Hausdorff距离和量子粒子群算法的二维图像匹配算法。为了实现二维图像的搜索，首先利用Canny算子提取图像的边缘，再利用Hausdorff距离作为图像搜索的目标函数，然后引入了带量子行为的粒子群的优化算法来求解搜索所需的空间变化参数，实验结果表明，带量子行为的粒子群的优化算法（QPSO）能够迅速地在全局范围内找到最优解，因此应用于二维图像搜索是可行的。

关键词: 二维图像搜索, Hausdorff距离, 量子粒子群算法

CLC Number:

TP391

ZHANG Yi¹，PENG Xiao-ming¹，CHEN Wu-fan^1，2. 2-D image matching based on Hausdorff distance and quantum particle swarm optimization[J]. Computer Engineering and Applications, 2010, 46(17): 179-181.

张毅¹，彭晓明¹，陈武凡^1，2. Hausdorff距离和量子粒子群的二维图像匹配[J]. 计算机工程与应用, 2010, 46(17): 179-181.

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2-D image matching based on Hausdorff distance and quantum particle swarm optimization

Hausdorff距离和量子粒子群的二维图像匹配

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