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计算机系统应用:2018,27(12):192-197
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基于局部聚类的特征匹配筛选算法
王金宝1,2, 赵奎2, 刘闽3, 宗子潇4, 王其乐5
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.沈阳市环境监测中心站, 沈阳 110016;4.东北大学 计算机科学与工程学院, 沈阳 110004;5.沈阳市第三十一中学, 沈阳 110021)
Filtering Algorithm of Feature Matching Based on Local Clustering
WANG Jin-Bao1,2, ZHAO Kui2, LIU Min3, ZONG Zi-Xiao4, WANG Qi-Le5
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.Shenyang Environmental Monitoring Center Station, Shenyang 110016, China;4.School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China;5.Shenyang Thirty-First Middle School, Shenyang 110021, China)
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投稿时间:2018-06-05    修订日期:2018-06-27
中文摘要: 特征匹配是图像拼接中的关键步骤之一,基于最邻近与次邻近欧氏距离比值的匹配算法往往存在大量的误匹配,好的筛选算法可以降低误匹配率提高处理效率,因此对于此类算法的研究具有重要意义.早期的RANSAC算法是一种被广泛使用筛选算法,但其存在迭代次数不确定,对BA过程不友好等缺陷.本文提出了一种全新的基于局部聚类思想的匹配筛选算法(LCMF).利用SURF和ORB提取特征点,使用最邻近算法进行匹配,之后利用LCMF算法进行筛选,实验表明,在使用ORB特征提取时,该算法可以获得较好的筛选效果.
中文关键词: 特征匹配  匹配筛选  局部聚类
Abstract:Feature matching is one of the key steps in image mosaic. The matching algorithm based on the best of two nearest matches often has a large number of mismatches. The good filtering algorithm can reduce the mismatch rate and improve the processing efficiency. Therefore, it is of great significance to study this kind of algorithm. The RANSAC algorithm is a widely used filtering algorithm, but it has many defects such as uncertain number of iterations and none of self-adaption in BA process. In this study, we propose a new filtering algorithm of Feature Matching based on Local Clustering (LCMF). The feature points are extracted by SURF and ORB, the BestOf2NearestMatcher algorithm is used to match, and then the LCMF algorithm is used to filter. The experiment shows that the algorithm can get better filtering result when ORB is used to extract feature.
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王金宝,赵奎,刘闽,宗子潇,王其乐.基于局部聚类的特征匹配筛选算法.计算机系统应用,2018,27(12):192-197
WANG Jin-Bao,ZHAO Kui,LIU Min,ZONG Zi-Xiao,WANG Qi-Le.Filtering Algorithm of Feature Matching Based on Local Clustering.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):192-197

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