Feature Matching Via Self Adjusting Reliable Correspondence Set and Early Termination

Feature matching is a fundamental task in remote sensing and 3D vision. In this paper, a new feature matching algorithm is proposed under the RANSAC interaction model in which the global RANSAC works on the initial correspondence set $\bf {C}$ and the local RANSAC works on the reliable local correspondence set which is initially constructed by removing outliers from $\bf {C}$ . To increase the matching accuracy, after each RANSAC interaction round, the proposed self adjusting strategy updates the local correspondence set adaptively by adding some potential correspondences from $\bf {C}$ , but removing some unreliable local correspondences. Combining the global and local confidence level conditions with our two early termination conditions, namely the local early termination condition and the global maximal RANSAC interaction round constraint, it can achieve the best compromise between matching accuracy and time for different inlier rate cases. Finally, we apply the weighted SVD-based method to estimate the global model solution. Based on 873 testing image pairs, comprehensive experimental results have justified the matching accuracy and execution time merits of our algorithm relative to the state-of-the-art methods.