Research on Adaptive ORB-SURF Image Matching Algorithm Based on Fusion of Edge Features

The traditional speeded-up robust features (SURF) algorithm has certain stability in scale, rotation, illumination and other changes. However, this algorithm has problems such as large amount of computation, low matching accuracy, and time-consuming in feature extraction and feature matching. An improved adaptive ORB-SURF image matching algorithm is proposed in this paper. Fusing edge features and using improved Oriented FAST and Rotated BRIEF (ORB) algorithms are used by this algorithm to extract image feature points. Moreover, SURF descriptors are used by feature points for feature description. Then an improved fast library for approximate nearest neighbors (FLANN) algorithm was used for adaptive feature matching. The random sample consensus (RANSAC) algorithm is used to eliminate the false matching point pairs after the selected points to be matched. Finally, the excellent matching point pairs reserved by the adaptive FLANN algorithm are combined with the excellent matching point pairs reserved by the RANSAC algorithm to complete the matching. The experimental results show that the average accuracy of the improved algorithm can reach more than 98%, which is about 6% higher than the original SURF algorithm. And the average matching time is 1.2S, which is about 25% lower than the original SURF algorithm. It is worth mentioning that the problem that the original SURF algorithm cannot predict the number of feature points through Hessian threshold is solved by this algorithm. Moreover, compared with the SuperPoint based deep learning image matching algorithm, the image matching time of this algorithm is reduced by 80%.