Fast Robust Image Feature Matching Algorithm Improvement and Optimization

被引：8

作者：

Chen, Peiyu ^{[1
]}

Li, Ying ^{[1
]}

Gong, Guanghong ^{[1
]}

机构：

[1] Beihang Univ, Adv Simulat Technol Key Lab, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON VISION, IMAGE AND SIGNAL PROCESSING (ICVISP 2018) | 2018年

关键词：

Feature points matching; ORB; SURF; Robustness;

D O I：

10.1145/3271553.3271585

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper quantitatively analyzes different types of image changes according to the characteristics of each algorithm, and put forward different optimal algorithms for different types of pictures. Firstly, four classical matching algorithms are selected and compared for scale, photometric and rotational robustness. In order to solve the limitation of the robustness of single algorithm, three improved algorithms are proposed. Based on the combination of SURF and ORB algorithms and one or more feature point screening, the improved algorithm is used to improve accuracy. Secondly, the improved algorithm is tested by using images with multiple types of changes at the same time. It is concluded that the improved algorithm has strong robustness and can effectively improve image matching accuracy. Finally, the simulation result shows that the selection of the optimal algorithm according to the features of the picture maximizes the advantages of different algorithms to meet the quantity of matching points and the matching accuracy.

引用

页数：5

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