Abstract
Registration is a core component in various applications of imaging and vision. While simple cases refer to the registration of clouds of points, a strong need exists for shape, image and volume alignment. In this paper, we propose a novel global-to-local registration method that integrates statistical and variational techniques. Registration is considered in an implicit higher dimensional space. The powerful space of distance transforms of arbitrary metric is used as an embedding function. Mutual information can support various motion models and is considered to perform global registration. A B-Spline approximation of grid is used within a free-from deformation criterion to recover a (complementary to the global) dense registration field that is continuous and guarantees one-to-one mapping. Such framework exhibits robustness and can cope in an efficient manner with important local deformations. 2D/3D shapes are used to demonstrate the potentials of the proposed technique.