Relaxation labeling for non-rigid point matching under neighbor preserving

Non-rigid point matching has received more and more attention. Recently, many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution. However, the local relationship among neighboring points is more effective under non-rigid transformations. Thus, a new algorithm taking advantage of shape context and relaxation labeling technique, called SC-RL, is proposed for non-rigid point matching. It is a strategy that joints estimation for correspondences as well as the transformation. In this work, correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient. The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system. The comparative analysis has been performed against four state-of-the-art algorithms including SC, ICP, TPS-RPM and RPM-LNS, and the results denote that SC-RL performs better in the presence of deformations, outliers and noise.