Noise stable image registration using RANdom RESAmple Consensus

Abstract

Image registration is an important and fundamental problem in computer vision and image processing. Although there are currently a large number of image registration algorithms such as RANSAC and its extensions, image registration under very noisy conditions remains difficult when it cannot obtain enough number of correct corresponding points. This paper solves this issue by introducing a random resample consensus (RANRESAC) strategy, which achieves robust registration where it is difficult to obtain enough numbers of correct correspondence pairs. In contrast to RANSAC, proposed RANRESAC newly generate corresponding points for the images using the hypothesis transformation function, and verifies the correctness by evaluating the similarity of the local features at the newly sampled points. To confirm the effectiveness for the proposed method, we first conducted an preliminary experiment that evaluates the similarity of texture and orientation components of SURF local descriptor in the images adding several levels of noise. As the result, we observed the texture component is more stable than the orientation component. Based on this finding, we design the RANRESAC algorithm and performed experiments using a open image registration dataset. As the result, proposed method outperforms to the RANSAC, MSAC and Optimal RANSAC algorithms in large noise conditions.