Abstract

Aiming at dimensional feature vector matching problem of low accuracy, a kind of image matching algorithm is based on SURF and fast library for approximate nearest neighbor search. Fast-Hessian was used to detect the feature points, and the SURF feature description vector proposed was generated; through fast library for approximate nearest neighbor search algorithm, pre-matching point was got. With the introduction of Random Sample Consensus (RANSAC) algorithm points false-matching, based on the SIFT algorithm, the result of SURF algorithm and optimization algorithm was proposed and mismatching points were eliminated after the experiment simulation. Experimental results show that at the same time the matching algorithm improve matching accuracy rate, and the algorithm of the real-time performance is also improved.

摘要

针对维数特征向量匹配精度不高的问题，提出了一种基于SURF和快速库的近似最近邻搜索的图像匹配算法。使用Fast-Hessian检测特征点，生成提出的SURF特征描述向量；通过近似最近邻搜索算法的快速库，得到预匹配点。引入随机样本共识（RANSAC）算法点假匹配，在SIFT算法的基础上，提出了SURF算法和优化算法的结果，并在实验模拟后剔除错配点。实验结果表明，该匹配算法在提高匹配准确率的同时，也提高了算法的实时性。