Spatially Coherent Matching for Robust Registration

In order to solve the registration problem, we propose a robust method called Spatially Coherent Matching (SCM), where it can get the underlying correspondences from the given putative sets of feature points for robust matching, and estimate the transformation for robust registration. Recovering correct matches and fitting transformations between image pairs are key components in the field of pattern recognition. The proposed SCM starts with a putative correspondence set which is contaminated by degradations (e.g., occlusion, deformation, rotation, and outliers), and the main goal is to identify the true correspondences and estimate the underlying transformation. Then we formulate this challenging problem by the spatially coherent matching model with a robust exponential distance loss and a spatial constraint. Based on the regularization theory, SCM preserves the topological structure of the adjacent features. Moreover, a sparse approximation strategy is used to improve the efficiency. Finally, the experimental results reveal that the proposed method outperforms current state-of-the-art methods in most test scenarios on several real image datasets and synthesized datasets.