纪松, 张永生, 范大昭, 龚志辉. 基于特征点引导的多视影像择优匹配方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(1): 37-45. DOI: 10.13203/j.whugis20150458
引用本文: 纪松, 张永生, 范大昭, 龚志辉. 基于特征点引导的多视影像择优匹配方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(1): 37-45. DOI: 10.13203/j.whugis20150458
JI Song, ZHANG Yongsheng, FAN Dazhao, GONG Zhihui. A Stereo Selecting Method of Multi-view Matching Models Guided Based on Feature Points[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 37-45. DOI: 10.13203/j.whugis20150458
Citation: JI Song, ZHANG Yongsheng, FAN Dazhao, GONG Zhihui. A Stereo Selecting Method of Multi-view Matching Models Guided Based on Feature Points[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 37-45. DOI: 10.13203/j.whugis20150458

基于特征点引导的多视影像择优匹配方法

A Stereo Selecting Method of Multi-view Matching Models Guided Based on Feature Points

  • 摘要: 从冗余数据中选择一个或者多个最为显著的立体像对,在最少“伪信息”的影响下,获取最佳影像匹配效果,降低其它质量较差影像的负面平均效应,是提高多视影像匹配性能的关键。基于准确匹配的特征点,通过匹配测度的鲁棒性分析,提出一种多视影像的匹配质量分析方法;在此基础上,提出了一种基于特征点引导的多视影像择优匹配方法及基本思想、计算基础和择优匹配步骤。利用ADS40多度重叠影像数据进行了择优匹配实验。结果表明,该方法能够有效选取匹配质量较优的影像,获取更加准确的多视匹配结果,在一定程度上,比传统的多视匹配方法更加有效。

     

    Abstract: Selecting one or more robust matching stereo pairs from redundant overlapping images to reduce the negative influence of incorrect or confusing image information to obtain the most desirable matching results improves multi-view matching ability and quality in multi-view matching techniques. In this paper, a matching quality analysis method for multi-view images is proposed that measures matching robustness based on correctly matched SIFT feature points. Furthermore, based on the method, a feature point guided multi-view image stereo selection matching method is detailed including the basic principles, algorithm, and matching process. Experiments were done on ADS40 multi-view imagery. The results show that the proposed method automatically and efficiently selects images of high matching quality from redundant overlapping images to obtain more correct multi-view matching results. This method is, to some extent, more effective than traditional multi-view matching methods.

     

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