A Framework for Image-Based Constrained Registration with an Application to Local Rigidity

Adding external knowledge improves the results for ill-posed prob-
lems. In this paper, we present a new computational framework
for image registration when adding constraints on the trans-
formation. We demonstrate that unconstrained registration can
lead to ambiguous and non-physical results. Adding appropriate
constraints introduces prior knowledge and contributes to reli-
ability and uniqueness of the registration. Particularly, we con-
sider recently proposed locally rigid transformations and volume
preserving constraints as examples.