An Adaptive Clustering Algorithm for Image Matching Based on Corner Feature

摘要

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on coiner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.