Diffeomorphisms Groups and Pattern Matching in Image Analysis
International Journal of Computer Vision
Variational problems on flows of diffeomorphisms for image matching
Quarterly of Applied Mathematics
Journal of Mathematical Imaging and Vision
Multigrid
Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Optimal registration of deformed images
Optimal registration of deformed images
A multilevel, level-set method for optimizing eigenvalues in shape design problems
Journal of Computational Physics
A Multilevel Method for Image Registration
SIAM Journal on Scientific Computing
FLIRT with Rigidity--Image Registration with a Local Non-rigidity Penalty
International Journal of Computer Vision
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Symmetric and transitive registration of image sequences
Journal of Biomedical Imaging
Diffeomorphic registration of images with variable contrast enhancement
Journal of Biomedical Imaging - Special issue on modern mathematics in biomedical imaging
Topology-preserving registration: a solution via graph cuts
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
A combined segmentation and registration framework with a nonlinear elasticity smoother
Computer Vision and Image Understanding
A Gauss-Newton approach to joint image registration and intensity correction
Computer Methods and Programs in Biomedicine
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The goal of image registration is twofold. One goal is to enforce a certain similarity of two images by geometrically transforming one of the images. The second goal is to keep this transformation meaningful or regular. There exists a large amount of approaches aiming for regularity. Most of those are based on certain regularization techniques, others use so-called regridding options.Here, we present a mathematically sound formulation that explicitly controls the deformation in terms of the determinant of the Jacobian of the transformation. In contrast to similar work, we use pointwise inequality constraints, i.e., the volume is controlled voxel by voxel and not by integral measures. This approach guaranties grid regularity and prevent folding.As it turns out, the discretization of the volume constraint inequality is not straightforward. Therefore, we present a new type of discretization enabling the detection of twists in a pixel or a voxel. Such detection is crucial since a twists indicates that a transformation is physically meaningless.To solve the large-scale inequality constrained optimization problem, we present a numerical approach based on an interior point method. We finally present some numerical examples that demonstrate the advantage of including inequality constraints explicitly.