Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
Multigrid
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Alignment by maximization of mutual information
Alignment by maximization of mutual information
A Multilevel Method for Image Registration
SIAM Journal on Scientific Computing
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Motion compensated video super resolution
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Brain image registration using cortically constrained harmonic mappings
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Using landmarks as a deformation prior for hybrid image registration
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
A landmark-based primal-dual approach for discontinuity preserving registration
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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Adding external knowledge improves the results for ill-posed problems. In this paper we present a new multi-level optimization framework for image registration when adding landmark constraints on the transformation. Previous approaches are based on a fixed discretization and lack of allowing for continuous landmark positions that are not on grid points. Our novel approach overcomes these problems such that we can apply multi-level methods which have been proven being crucial to avoid local minima in the course of optimization. Furthermore, for our numerical method we are able to use constraint elimination such that we trace back the landmark constrained problem to a unconstrained optimization leading to an efficient algorithm.