Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
MRF Solutions for Probabilistic Optical Flow Formulations
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Layered Motion Segmentation of Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A New Framework for Approximate Labeling via 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
Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
As-rigid-as-possible image registration for hand-drawn cartoon animations
Proceedings of the 7th International Symposium on Non-Photorealistic Animation and Rendering
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
Towards more efficient and effective LP-based algorithms for MRF optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Joint registration and segmentation of dynamic cardiac perfusion images using MRFs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Computer Vision and Image Understanding
Completely Convex Formulation of the Chan-Vese Image Segmentation Model
International Journal of Computer Vision
Globally Optimal Estimation of Nonrigid Image Distortion
International Journal of Computer Vision
Automatic coin classification by image matching
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
Analytical dynamic programming matching
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Adaptive large window correlation for optical flow estimation with discrete optimization
Image and Vision Computing
Computer Vision and Image Understanding
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We propose a novel MRF-based model for deformable image matching (also known as registration). The deformation is described by a field of discrete variables, representing displacements of (blocks of) pixels. Discontinuities in the deformation are prohibited by imposing hard pairwise constraints in the model. Exact maximum a posteriori inference is intractable and we apply a linear programming relaxation technique. We show that, when reformulated in the form of two coupled fields of x- and y-displacements, the problem leads to a simpler relaxation to which we apply the sequential tree-reweighted message passing (TRW-S) algorithm [Wainwright-03, Kolmogorov-05]. This enables image registration with large displacements at a single scale. We employ fast message updates for a special type of interaction as was proposed [Felzenszwalb and Huttenlocher-04] for the max-product belief propagation (BP) and introduce a few independent speedups. In contrast to BP, the TRW-S allows us to compute per-instance approximation ratios and thus to evaluate the quality of the optimization. The performance of our technique is demonstrated on both synthetic and real-world experiments.