Free-form deformation of solid geometric models
SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Approximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Reliability-driven, spatially-adaptive regularization for deformable registration
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
Rolled fingerprint construction using MRF-based nonrigid image registration
IEEE Transactions on Image Processing
TriangleFlow: optical flow with triangulation-based higher-order likelihoods
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Solving MRFs with higher-order smoothness priors using hierarchical gradient nodes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Computer Vision and Image Understanding
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In this paper, we propose a nonrigid registration method using the Markov Random Field (MRF) model with a higher-order spatial prior. The registration is designed as finding a set of discrete displacement vectors on a deformable mesh, using the energy model defined by label sets relating to these vectors. This work provides two main ideas to improve the reliability and accuracy of the registration. First, we propose a new energy model which adopts a higher-order spatial prior for the smoothness cost. This model improves limitations of pairwise spatial priors which cannot fully incorporate the natural smoothness of deformations. Next we introduce a dynamicenergy model to generate optimal displacements. This model works iteratively with optimal data cost while the spatial prior preserve the smoothness cost of previous iteration. For optimization, we convert the proposed model to pairwise MRF model to apply the tree-reweighted message passing (TRW). Concerning the complexity, we apply the decomposedscheme to reduce the label dimension of the proposed model and incorporate the linear constrained node (LCN) technique for efficient message passings. In experiments, we demonstrate the competitive performance of the proposed model compared with previous models, presenting both quantitative and qualitative analysis.