激光与光电子学进展, 2021, 58 (8): 0810025, 网络出版: 2021-04-12
基于Pearson相关系数的图像误匹配点剔除算法 下载: 1039次
Algorithm for Eliminating Mismatched Points Based on Pearson Correlation Coefficient
图像处理 去除误匹配点 Pearson相关系数 特征点匹配 image processing eliminating mismatched point Pearson correlation coefficient feature point matching
摘要
在目标识别和图像配准等领域中,进行特征点匹配时一般都会产生误匹配点,对误匹配点的准确剔除可以有效提升识别精度及配准精度,因而成为研究的重点。当前比较成熟的剔除算法,如random sample consensus(RANSAC)、M-estimator sample consensus(MSAC)等,经常会出现剔除部分正确匹配点的情况。针对当前存在的问题,提出了一种基于Pearson相关系数,对长度和夹角进行双约束的误匹配点剔除算法。所提算法首先粗剔除误差较大的误匹配点,进而通过迭代的方式对误差较小的误匹配点进行精细剔除。多幅图像的实验结果证明,所提算法能在剔除全部误匹配点的基础上保留绝大部分正确匹配点,与对比组算法相比,保留正确匹配点的比例更高,有效地降低了误剔除率,对提升图像匹配的准确度具有重要意义。
Abstract
Mismatched points are inevitable when matching the feature points in target recognition and image registration. The proper elimination of mismatched points improves the accuracy of recognition and registration, therefore, has become a focus of this research field. The currently mature elimination algorithms, such as random sample consensus (RANSAC) and M-estimator sample consensus (MSAC), often eliminate some of the correctly matched points. To overcome this shortcoming, this study proposes a mismatched-point elimination algorithm with double constraints on length and included angle based on the Pearson correlation coefficient. First, the mismatched points with larger error are roughly eliminated, and the mismatched points with smaller error are then precisely eliminated by iteration. In comparative experiments on several images, the proposed algorithm retained most of the correctly matched points while eliminating all of the wrong matched points. This performance was not matched by the comparative algorithms RANSAC and MSAC. Therefore, the proposed algorithm greatly reduces the error elimination rate and can significantly improve the accuracy of image matching.
李硕, 韩迎东, 王双, 刘琨, 江俊峰, 刘铁根. 基于Pearson相关系数的图像误匹配点剔除算法[J]. 激光与光电子学进展, 2021, 58(8): 0810025. Shuo Li, Yingdong Han, Shuang Wang, Kun Liu, Junfeng Jiang, Tiegen Liu. Algorithm for Eliminating Mismatched Points Based on Pearson Correlation Coefficient[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810025.