Image registration using two-layer cascade reciprocal pipeline and context-aware dissimilarity measure

Abstract

We present a method based on a robust two-layer cascade reciprocal pipeline (TCPR) and the context-aware dissimilarity measure (CADM) for feature-based image registration. We first build the two-layer pipeline to find the optimal correspondence and to achieve the image transformation. In the first layer, the neighborhood structures of point sets are used to find and register reliable feature point sets. In the second layer, we create the point-to-point neighborhood structure to recover the remaining inliers for image registration. The CADM with scaling, translation and rotation invariance is then designed for dissimilarity evaluation. Extensive experiments regarding feature matching, image registration and retrieval on real and synthesized images demonstrate that our method can achieve a better accuracy–efficiency tradeoff compared with ten state-of-the-art methods.