Robust non-rigid point set registration via building tree dynamically

Abstract

The non-rigid registration methods, such as coherent point drift (CPD) method can deal with similar point sets, but it is difficult for them to achieve the non-rigid registration of point sets with large deformations. To overcome the problem, a novel approach via building dynamic tree is proposed in this paper. First of all, the similarity between the model and subject point sets is evaluated by the affine iterative closest point (ICP) algorithm with bidirectional distance, and the models and their similar subjects are connected. Secondly, the non-rigid registration is conducted on every two similar point sets. The subjects with accurate registration results are added to the model sets and wrong pairs are cut off based on a bidirectional distance. These steps are repeated and a dynamic tree is built up. In this way, a large deformation between two images is decomposed into a series of small deformations and the elimination of the wrong pairs in the dynamic tree guarantees the registration results are precise and satisfactory. Experimental results on several image datasets demonstrate that our method improves the accuracy of the point set registration results with large shape difference compared with existing approaches.