An improved SURF method on the visual image area

doi:10.13474/j.cnki.11-2246.2021.0018

Abstract

Abstract: Visual images always have the problem of complex texture and dynamic blur, and seriously reduces the similarity of various features between continuous images, and makes it difficult for traditional matching algorithms to obtain accurate, stable and well distributed image matching, then affects the acquisition of all kinds of information in subsequent image processing. In order to solve these problems, this paper proposes an improved surf visual image matching method. The method includes three steps: feature extraction, initial matching and corresponding matching. Firstly, SURF feature matching method is used to extract enough and well distributed feature points; secondly, initial matching is carried out to obtain some correct matching point pairs and the initial projection transformation relationship between image pairs; finally, geometric correspondence matching strategy is used for matching propagation to obtain more reliable matching results. Through the geometric relationship between image pairs, geometric correspondence matching can find more suitable matching results than the original surf algorithm. The experimental results of the TUM data synthesis show that the algorithm is simple and fast, and the matching accuracy is high.

Key words: SURF, visual image, image matching, feature extraction, geometric correspondence matching

CLC Number:

P237

QIAN Xuefei, SHEN Yingzheng, WANG Youkun, CHEN Yu, XU Bo. An improved SURF method on the visual image area[J]. Bulletin of Surveying and Mapping, 2021, 0(1): 99-102,107.

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