| 基于最佳几何约束和RANSAC的特征匹配算法 |
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| 引用本文： | 宁小娟,李洁茹,高凡,王映辉. 基于最佳几何约束和RANSAC的特征匹配算法[J]. 系统仿真学报, 2022, 34(4): 727-734. DOI: 10.16182/j.issn1004731x.joss.21-0857 |
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| 作者姓名： | 宁小娟 李洁茹 高凡 王映辉 |
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| 作者单位： | 1.西安理工大学 计算机科学与工程学院, 陕西 西安 7100482.陕西省网络计算与安全技术重点实验室, 陕西 西安 7100483.江南大学 人工智能与计算机学院, 江苏 无锡 214122 |
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| 基金项目： | 国家自然科学基金(61871320);国家重点研发计划(2018YFB1004905);教育厅重点实验室项目(17JS099) |
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| 摘 要： | 为解决特征点匹配的质量与计算效率不能兼得的问题，研究了一种基于最佳几何约束和RANSAC(random sample consensus)的特征点匹配方法。采用KNN (k-nearest neighbor)算法对提取到的特征点完成初始匹配，根据匹配点对连接线长度相等、斜率相同的特点，基于统计排序策略构建最佳几何约束，剔除明显错误匹配。利用RANSAC算法进行二次过滤，确保特征匹配点对的正确率，同时给出实验结果加以验证。结果表明：在正常光照下，与Lowe’s算法和GMS算法相比，该算法匹配到的点对数有了明显增加，同时很大程度上保证了特征点的质量。
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| 关 键 词： | 统计排序 最佳几何约束 RANSAC(random sample consensus)算法 特征点匹配 |
| 收稿时间： | 2021-08-24 |
Feature Matching Algorithm Based on Optimal Geometric Constraints and RANSAC |
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| Xiaojuan Ning,Jieru Li,Fan Gao,Yinghui Wang. Feature Matching Algorithm Based on Optimal Geometric Constraints and RANSAC[J]. Journal of System Simulation, 2022, 34(4): 727-734. DOI: 10.16182/j.issn1004731x.joss.21-0857 |
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| Authors: | Xiaojuan Ning Jieru Li Fan Gao Yinghui Wang |
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| Affiliation: | 1.Institute of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China2.Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an 710048, China3.Institute of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China |
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| Abstract: | In order to solve the problem that it's hard to reconcile the quality and computational efficiency of feature point matching. The initial matching for the extracted feature points is implemented through k-nearest neighbor (KNN) algorithm. According to the characteristics of equal length and same slope of the connecting line between matching points, the optimal geometric constraint is constructed based on the statistical sorting strategy to eliminate the obvious matching errors. Then random sample consensus (RANSAC) algorithm is utilized for further filtering to ensure the accuracy of the feature matching point pairs. Experimental results show that the method can obtain more matched point pairs under normal light, compared with Lowe's algorithm and GMS algorithm, and can ensure the quality of feature points. |
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| Keywords: | statistical ranking optimal geometric constraint random sample consensus algorithm feature point match |
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