Konard Simon (Weizmann Institute of Science)
Shape matching is the problem of finding a meaningful transformation between two (or more) shapes that have a priori unknown correspondences. It is the key ingredient for many applications in computer vision, medical imaging and graphics. These applications comprise, for example, shape interpolation, shape retrieval, information transfer and the alignment of scanned data. We propose a method that is based on the physical theory of nonlinear elasticity and is hence a suitable framework for large rotations and deformations. Deformation boundary conditions that supplement the underlying equations are usually unknown. We formulate an optimization problem to account for these unknowns and show a heuristic way to approximate the nonlinear optimization problem by a sequence of convex problems in a small and large deformation framework, in both, two and three dimensions.