Abstract

This paper deals with the matching of geometric shapes. Our primary contribution is the use of a simple, robust, rich and e?- cient way to represent shapes, the level set representations according to singed distance transforms. Based on these representations we propose a variational framework for global as well as local shape registration that can be extended to deal with structures of higher dimension. The op- timization criterion is invariant to rotation, translation and scale and combines e?ciently a global motion model with local pixel-wise defor- mations. Promising results are obtained on examples showing small and large global deformations as well as arbitrary topological changes.