A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences

Abstract

The nonrigid point set registration is one of the bottlenecks and has the wide applications in computer vision, pattern recognition, image fusion, video processing, and so on. In a nonrigid point set registration problem, finding the point-to-point correspondences is challengeable because of the various image degradations. In this paper, a robust method is proposed to accurately determine the correspondences by fusing the two complementary structural features, including the spatial location of a point and the local structure around it. The former is used to define the absolute distance (AD), and the latter is exploited to define the relative distance (RD). The AD-correspondences and the RD-correspondences can be established based on AD and RD, respectively. The neighboring corresponding consistency is employed to assign the confidence for each RD-correspondence. The proposed heuristic method combines the AD-correspondences and the RD-correspondences to determine the corresponding relationship between two point sets, which can significantly improve the corresponding accuracy. Subsequently, the thin plate spline (TPS) is employed as the transformation function. At each step, the closed-form solutions of the affine and nonaffine parts of TPS can be independently and robustly solved. It facilitates to analyze and control the registration process. Experimental results demonstrate that our method can achieve better performance than several existing state-of-the-art methods.

Enhancing aerial image registration: outlier filtering through feature classification

“…They increased registration accuracy from 2.4 to 53.7%. Feng and Feng [8] proposed an image registration technique that handles intensity and geometric transformations. They introduced two novel assessment factors, "salience correlation" and "parsimony," to evaluate alignment accuracy.…”

Data, Signal and Image Processing and Applications in Sensors

“…Feng and Feng [ 13 ] proposed a robust method to accurately determine the correspondences by fusing two complementary structural features, including the spatial location of a point and the local structure around it. Experimental results demonstrate that the method can achieve a better performance than several existing state-of-the-art methods.…”