Multi-source image matching using improved ORB algorithm

Image matching is an essential technique in computer vision for many applications (e.g., image understanding). In order to improve the adaptability of previous matching algorithms to multi-source images (i.e., perspective and panoramic images) and increase the matching accuracy, we propose an improve Oriented FAST and Rotated BRIEF (ORB) matching algorithm. We first use the K-Nearest Neighbor (KNN) algorithm to roughly match the feature points extracted from the uniformly partitioned image grids and calculate their matching scores to obtain high scoring matching pairs. Then, we utilize the regional local consistency constraint and affine transformation verification of the Adaptive Local Affine Matching (AdaLAM) method to further refine the matching pairs. Finally, we perform matching experiments on both perspective and panoramic images and show better matching results than most previous matching approaches.