Whole-pixel registration of non-rigid images using correspondences interpolation on sparse feature seeds

Abstract

Whole pixel registration of non-rigid images with high accuracy and efficiency is a challenging problem in computer vision. To address this issue, we propose a correspondence vector field (CVF) Interpolation approach based on sparse matching of feature seeds. First, we detect and match two types of feature seeds to improve the accuracy of the later dense CVF interpolation. The first type of feature seeds is to guarantee the accuracy of the motion boundary, while the second one is to achieve the uniform distribution of seeds, which is helpful to improve the effect of interpolation. Second, we regionally estimate the dense CVF using the proposed interpolation approach on this basis. At last, we realize the whole-pixel registration of non-rigid images to yield the image alignment. Different from the traditional CVF interpolation approaches based on optical flow field, ours is based on the sparse matching of feature seeds. Thus, it is not limited to the large displacements and tends to achieve the accurate matching of certain key points easily, which is critical to the final interpolation result. Qualitative and quantitative experimental results on several internationally used datasets demonstrate that our approach outperforms the state-of-the-art ones.