A Stochastic Approach to Diffeomorphic Point Set Registration with Landmark Constraints

Citation:

Ivan Kolesov, Jehoon Lee, Gregory Sharp, Patricio Vela, and Allen Tannenbaum. 2016. “A Stochastic Approach to Diffeomorphic Point Set Registration with Landmark Constraints.” IEEE Trans Pattern Anal Mach Intell, 38, 2, Pp. 238-51.

Abstract:

This work presents a deformable point set registration algorithm that seeks an optimal set of radial basis functions to describe the registration. A novel, global optimization approach is introduced composed of simulated annealing with a particle filter based generator function to perform the registration. It is shown how constraints can be incorporated into this framework. A constraint on the deformation is enforced whose role is to ensure physically meaningful fields (i.e., invertible). Further, examples in which landmark constraints serve to guide the registration are shown. Results on 2D and 3D data demonstrate the algorithm's robustness to noise and missing information.
Last updated on 01/24/2017