A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
A Feature Registration Framework Using Mixture Models
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Setting Priors and Enforcing Constraints on Matches for Nonlinear Registration of Meshes
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper, we present a new algorithm for non-linear registration of point sets. We estimate both forward and backward deformations fields best superposing the two point sets of interest and we make sure that they are consistent with each other by designing a symmetric cost function where they are coupled. Regularisation terms are included in this cost function to enforce deformation smoothness. Then we present a two-step iterative algorithm to optimise this cost function, where the two fields and the fuzzy matches between the two sets are estimated in turn. Building regularisers using the RKHS theory allows to obtain fast and efficient closed-form solutions for the optimal fields. The resulting algorithm is efficient and can deal with large point sets.