Fast Robust Image Feature Matching Algorithm Improvement and Optimization

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.