Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
Active shape models—their training and application
Computer Vision and Image Understanding
Landmark-Based Image Analysis: Using Geometric and Intensity Models
Landmark-Based Image Analysis: Using Geometric and Intensity Models
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Snakes and Splines for Tracking Non-Rigid Heart Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Consistent Nonlinear Elastic Image Registration
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Alignment by maximization of mutual information
Alignment by maximization of mutual information
Probability Measures on Semigroups: Convolution Products, Random Walks and Random Matrices
Probability Measures on Semigroups: Convolution Products, Random Walks and Random Matrices
Large deformation diffeomorphisms with application to optic flow
Computer Vision and Image Understanding
Symmetrical Dense Optical Flow Estimation with Occlusions Detection
International Journal of Computer Vision
Brownian Warps for Non-Rigid Registration
Journal of Mathematical Imaging and Vision
Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation
Journal of Mathematical Imaging and Vision
Spherical Demons: Fast Surface Registration
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
| Hi-index | 0.00 |
Non-rigid registration requires a smoothness or regularization term for making the warp field regular. Standard models in use here include b-splines and thin plate splines. In this paper, we suggest a regularizer which is based on first principles, is symmetric with respect to source and destination, and fulfills a natural semi-group property for warps. We construct the regularizer from a distribution on warps. This distribution arises as the limiting distribution for concatenations of warps just as the Gaussian distribution arises as the limiting distribution for the addition of numbers. Through an Euler-Lagrange formulation, algorithms for obtaining maximum likelihood registrations are constructed. The technique is demonstrated using 2D examples.