The generalized distributive law, IEEE Transactions on Information Theory, vol.46, issue.2, pp.325-343, 2000. ,
DOI : 10.1109/18.825794
Reduce, reuse & recycle: Efficiently solving multi-label MRFs, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587402
Gimel'farb. Optimizing binary MRFs with higher order cliques, European Conference on Computer Vision (ECCV), 2008. ,
Using dynamic programming for solving variational problems in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.12, issue.9, pp.855-867, 1990. ,
Pictorial structures revisited: People detection and articulated pose estimation, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206754
The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces, Advances in Neural Information Processing Systems (NIPS), 2004. ,
Motion segmentation with occlusions on the superpixel graph, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009. ,
DOI : 10.1109/ICCVW.2009.5457630
Beyond trees: MRF inference via outer-planar decomposition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539951
Making the right moves: Guiding alpha-expansion using local primal-dual gaps, CVPR 2011, 2011. ,
DOI : 10.1109/CVPR.2011.5995449
Dynamic Programming, 1957. ,
Nonlinear Programming (Second Edition), Athena Scientific, 1999. ,
Spatial Interaction and the Statistical Analysis of Lattice Systems, Journal of the Royal Statistical Society. Series B (Methodological), vol.36, issue.2, pp.192-236, 1974. ,
On the Statistical Analysis of Dirty Pictures Julian Besag (with discussion), Journal of the Royal Statistical Society (Series B), vol.48, issue.3, pp.259-302, 1986. ,
Shape priors and discrete MRFs for knowledge-based segmentation, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206649
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.195
Neural networks for pattern recognition, 1995. ,
Pattern recognition and machine learning (Information Science and Statistics), 2006. ,
Visual Reconstruction, 1987. ,
Distance transformations in digital images Computer vision, graphics, and image processing, pp.344-371, 1986. ,
Network flows and minimization of quadratic pseudo-Boolean functions, 1991. ,
Preprocessing of unconstrained quadratic binary optimization, 2006. ,
Pseudo-Boolean optimization, Discrete Applied Mathematics, vol.123, issue.1-3, pp.155-225, 2002. ,
DOI : 10.1016/S0166-218X(01)00341-9
URL : https://hal.archives-ouvertes.fr/hal-01150533
Convex Optimization, 2004. ,
Graph Cuts and Efficient N-D Image Segmentation, International Journal of Computer Vision, vol.18, issue.9, pp.109-131, 2006. ,
DOI : 10.1007/s11263-006-7934-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.90.657
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001. ,
DOI : 10.1109/ICCV.2001.937505
Computing geodesics and minimal surfaces via graph cuts, Proceedings Ninth IEEE International Conference on Computer Vision, 2003. ,
DOI : 10.1109/ICCV.2003.1238310
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.6433
Markov random fields with efficient approximations, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1998. ,
DOI : 10.1109/CVPR.1998.698673
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.3857
Fast approximate energy minimization via graph cuts, International Conference on Computer Vision (ICCV), 1999. ,
DOI : 10.1109/iccv.1999.791245
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806
Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001. ,
DOI : 10.1109/34.969114
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806
Minimizing energy functions on 4-connected lattices using elimination, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459450
Total Variation Minimization and a Class of Binary MRF Models, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2005. ,
DOI : 10.1007/11585978_10
Image processing and analysis: variational, PDE, wavelet, and stochastic methods, Society for Industrial and Applied Mathematics, 2005. ,
Probabilistic network inference for cooperative high and low level vision, Markov Random Fields: Theory and Applications, pp.211-243, 1993. ,
The theory and practice of Bayesian image labeling, International Journal of Computer Vision (IJCV), vol.4, issue.3, pp.185-210, 1990. ,
Introduction to algorithms, 2009. ,
The complexity of multiway cuts (extended abstract), Proceedings of the twenty-fourth annual ACM symposium on Theory of computing , STOC '92, 1992. ,
DOI : 10.1145/129712.129736
Applications of a general propagation algorithm for probabilistic expert systems, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.25-36, 1992. ,
DOI : 10.1007/BF01890546
Image processing with neural networks???a review, Pattern Recognition, vol.35, issue.10, pp.2279-2301, 2002. ,
DOI : 10.1016/S0031-3203(01)00178-9
Better appearance models for pictorial structures, Procedings of the British Machine Vision Conference 2009, 2009. ,
DOI : 10.5244/C.23.3
Regularization of inverse problems, 1996. ,
Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010. ,
DOI : 10.1109/TPAMI.2009.167
Pictorial Structures for Object Recognition, International Journal of Computer Vision (IJCV), vol.61, issue.1, pp.55-79, 2005. ,
Efficient Belief Propagation for Early Vision, International Journal of Computer Vision (IJCV), vol.70, issue.1, pp.41-54, 2006. ,
Fast Inference with Min-Sum Matrix Product, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.33, issue.12, pp.2549-2554, 2011. ,
Dynamic programming and graph algorithms in computer vision, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.33, issue.4, pp.721-740, 2011. ,
The Representation and Matching of Pictorial Structures, IEEE Transactions on Computers, vol.22, issue.1, pp.67-92, 1973. ,
DOI : 10.1109/T-C.1973.223602
A graph cut algorithm for higher-order Markov Random Fields, 2011 International Conference on Computer Vision, 2011. ,
DOI : 10.1109/ICCV.2011.6126347
Energy Minimization via Graph Cuts: Settling What is Possible, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005. ,
DOI : 10.1109/CVPR.2005.143
Example-based super-resolution, IEEE Computer Graphics and Applications, vol.22, issue.2, pp.56-65, 2002. ,
DOI : 10.1109/38.988747
Learning low-level vision, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.25-47, 2000. ,
DOI : 10.1109/ICCV.1999.790414
Graphical models for machine learning and digital communication, 1998. ,
A revolution: Belief propagation in graphs with cycles, Advances in Neural Information Processing Systems (NIPS), 1998. ,
Class segmentation and object localization with superpixel neighborhoods, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459175
Inference for Order Reduction in Markov Random Fields, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. ,
Identifying independence in bayesian networks, Networks, vol.9, issue.5, pp.507-534, 1990. ,
DOI : 10.1002/net.3230200504
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.6, issue.6, pp.721-741, 1984. ,
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations, Advances in Neural Information Processing Systems (NIPS), 2007. ,
TriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15558-1_20
Dense image registration through MRFs and efficient linear programming???, Medical Image Analysis, vol.12, issue.6, pp.731-741, 2008. ,
DOI : 10.1016/j.media.2008.03.006
Optical flow estimation with uncertainties through dynamic MRFs, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587562
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6681
A new approach to the maximum-flow problem, Journal of the ACM, vol.35, issue.4, pp.921-940, 1988. ,
DOI : 10.1145/48014.61051
Exact Maximum A Posteriori Estimation for Binary Images, Journal of the Royal Statistical Society (Series B), vol.51, issue.2, pp.271-279, 1989. ,
Cooperative Object Segmentation and Behavior Inference in??Image Sequences, International Journal of Computer Vision, vol.127, issue.3, pp.146-162, 2008. ,
DOI : 10.1007/s11263-008-0146-4
Roof duality, complementation and persistency in quadratic 0???1 optimization, Mathematical Programming, pp.121-155, 1984. ,
DOI : 10.1007/BF02612354
Markov fields on finite graphs and lattices. unpublished, 1971. ,
Multiscale conditional random fields for image labeling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004. ,
Statistical shape models for 3D medical image segmentation: a review Multimodal estimation of discontinuous optical flow using Markov random fields, Medical Image Analysis IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.13, issue.412, pp.543-63, 1993. ,
A HMM-Based Method for Recognizing Dynamic Video Contents from Trajectories, 2007 IEEE International Conference on Image Processing, 2007. ,
DOI : 10.1109/ICIP.2007.4380072
PAMPAS: real-valued graphical models for computer vision, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003. ,
DOI : 10.1109/CVPR.2003.1211410
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.151
Exact optimization for markov random fields with convex priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.1333-1336, 2003. ,
DOI : 10.1109/TPAMI.2003.1233908
Higher-order clique reduction in binary graph cut, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206689
Transformation of General Binary MRF Minimization to the First-Order Case, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.6, pp.1234-1249, 2011. ,
DOI : 10.1109/TPAMI.2010.91
Segmentation by grouping junctions, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1998. ,
DOI : 10.1109/CVPR.1998.698598
Beitrag zur theorie des ferromagnetismus. Zeitschrift fur Physik, pp.253-258, 1925. ,
DOI : 10.1007/bf02980577
Accelerated dual decomposition for MAP inference, International Conference on Machine Learning (ICML), 2010. ,
An introduction to probabilistic graphical models. In preparation, 2007. ,
Active Graph Cuts, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006. ,
DOI : 10.1109/CVPR.2006.47
A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960. ,
DOI : 10.1115/1.3662552
Learning 3d mesh segmentation and labeling, ACM Transactions on Graphics (TOG), vol.29102, issue.4, pp.1-10212, 2010. ,
DOI : 10.1145/1833349.1778839
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.179.640
MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation, European Conference on Computer Vision (ECCV), 2010. ,
Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988. ,
DOI : 10.1007/BF00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318
Spatio-temporal adaptive 3-D Kalman filter for video, IEEE Transactions on Image Processing (TIP), vol.6, issue.3, pp.414-424, 1997. ,
Inference in bayesian networks using nested junction trees, Proceedings of the NATO Advanced Study Institute on Learning in graphical models, 1998. ,
Robust higher order potentials for enforcing label consistency, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. ,
Robust Higher Order Potentials for Enforcing Label Consistency, International Journal of Computer Vision, vol.24, issue.3, pp.302-324, 2009. ,
DOI : 10.1007/s11263-008-0202-0
Energy minimization for linear envelope MRFs, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539858
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.999
P3 & Beyond: Solving Energies with Higher Order Cliques, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383204
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.119.2624
P3 & beyond: move making algorithms for solving higher order functions, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), issue.9, pp.311645-1656, 2009. ,
Simultaneous Segmentation and Pose Estimation of Humans Using??Dynamic Graph Cuts, International Journal of Computer Vision, vol.57, issue.3, pp.285-298, 2008. ,
DOI : 10.1007/s11263-007-0120-6
On partial optimality in multi-label MRFs, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008. ,
DOI : 10.1145/1390156.1390217
Efficiently solving dynamic Markov random fields using graph cuts, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005. ,
DOI : 10.1109/ICCV.2005.81
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, pp.2079-2088, 2007. ,
DOI : 10.1109/TPAMI.2007.1128
Measuring uncertainty in graph cut solutions, Computer Vision and Image Understanding, vol.112, issue.1, pp.30-38, 2008. ,
DOI : 10.1016/j.cviu.2008.07.002
Probabilistic graphical models: Principles and techniques, 2009. ,
Convergent Tree-Reweighted Message Passing for Energy Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1568-1583, 2006. ,
DOI : 10.1109/TPAMI.2006.200
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2409
Minimizing Nonsubmodular Functions with Graph Cuts-A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1274-1279, 2007. ,
DOI : 10.1109/TPAMI.2007.1031
On the optimality of tree-reweighted maxproduct message-passing, Conference on Uncertainty in Artificial Intelligence (UAI), 2005. ,
What energy functions can be minimized via graph cuts?, European Conference on Computer Vision (ECCV), pp.147-159, 2002. ,
DOI : 10.1109/TPAMI.2004.1262177
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1823
Towards More Efficient and Effective LP-Based Algorithms for MRF Optimization, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15552-9_38
Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles, European Conference on Computer Vision (ECCV), 2008. ,
DOI : 10.1007/978-3-540-88690-7_60
URL : https://hal.archives-ouvertes.fr/hal-00918715
Beyond pairwise energies: Efficient optimization for higher-order MRFs, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206846
MRF Optimization via Dual Decomposition: Message-Passing Revisited, 2007 IEEE 11th International Conference on Computer Vision, 2007. ,
DOI : 10.1109/ICCV.2007.4408890
MRF Energy Minimization and Beyond via Dual Decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.531-552, 2011. ,
DOI : 10.1109/TPAMI.2010.108
URL : https://hal.archives-ouvertes.fr/hal-00856311
Approximate Labeling via Graph Cuts Based on Linear Programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, pp.1436-1453, 2007. ,
DOI : 10.1109/TPAMI.2007.1061
Fast, Approximately Optimal Solutions for Single and Dynamic MRFs, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383095
Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies, Computer Vision and Image Understanding, vol.112, issue.1, pp.14-29, 2008. ,
DOI : 10.1016/j.cviu.2008.06.007
URL : https://hal.archives-ouvertes.fr/hal-00918699
Dvumernoe programmirovanie v zadachakh analiza izobrazheniy (Two-dimensional programming in image analysis problems), pp.149-168, 1976. ,
A diffusion algorithm for decreasing energy of max-sum labeling problem, Glushkov Institute Of Cybernetics, 1975. ,
Efficient inference in fully connected crfs with gaussian edge potentials, Advances in Neural Information Processing Systems (NIPS), 2011. ,
Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory, vol.47, issue.2, pp.498-519, 2001. ,
Discriminative fields for modeling spatial dependencies in natural images, Advances in Neural Information Processing Systems (NIPS), 2004. ,
Nonrigid Image Registration Using Dynamic Higher-Order MRF Model, European Conference on Computer Vision (ECCV), 2008. ,
DOI : 10.1007/978-3-540-88682-2_29
Associative hierarchical CRFs for object class image segmentation, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459248
Graph Cut Based Inference with Co-occurrence Statistics, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15555-0_18
Inference Methods for CRFs with Co-occurrence Statistics, International Journal of Computer Vision, vol.103, issue.2, 2011. ,
DOI : 10.1007/s11263-012-0583-y
What, Where and How Many? Combining Object Detectors and CRFs, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15561-1_31
URL : https://hal.archives-ouvertes.fr/hal-01216730
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, International Conference on Machine Learning (ICML), 2001. ,
Efficient Belief Propagation with Learned Higher-Order Markov Random Fields, European Conference on Computer Vision (ECCV), 2006. ,
DOI : 10.1109/TIP.2003.819861
Graphical Models, 1996. ,
One-dimensional regularization with discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.10, issue.6, pp.822-829, 1988. ,
DOI : 10.1109/34.9105
Fusion Moves for Markov Random Field Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1392-1405, 2010. ,
DOI : 10.1109/TPAMI.2009.143
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.1633
Optimal Contour Closure by Superpixel Grouping, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15552-9_35
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.228.2448
Markov random field modeling in image analysis, 2009. ,
DOI : 10.1007/978-4-431-67044-5
SIFT Flow: Dense Correspondence Across Scenes and Its Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.33, issue.5, pp.978-994, 2011. ,
DOI : 10.1007/978-3-319-23048-1_2
Faster Algorithms for Max-Product Message- Passing, Journal of Machine Learning Research, vol.12, pp.1349-1388, 2011. ,
HMM based hand gesture recognition: A review on techniques and approaches, 2009 2nd IEEE International Conference on Computer Science and Information Technology, 2009. ,
DOI : 10.1109/ICCSIT.2009.5234536
Lattice Cut" -Constructing superpixels using layer constraints, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. ,
Global connectivity potentials for random field models, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206567
Thin Junction Tree Filters for Simultaneous Localization and Mapping, International Joint Conference on Artificial Intelligence (IJCAI), 2003. ,
Dynamic Bayesian Networks for Information Fusion with Application to Human-Computer Interfaces, 1999. ,
Combinatorial and Convex Optimization for Probabilistic Models in Computer Vision, 2008. ,
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs, Journal of Machine Learning Research, vol.10, pp.71-106, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00773608
Fast memory-efficient generalized belief propagation, European Conference on Computer Vision (ECCV), 2006. ,
Learning Layered Pictorial Structures from Video, The Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2004. ,
Probabilistic reasoning in intelligent systems: networks of plausible inference, 1988. ,
Causality: Models, Reasoning, and Inference, 2009. ,
DOI : 10.1017/CBO9780511803161
Fast generalized belief propagation for MAP estimation on 2D and 3D grid-like markov random fields. DAGM-Symposium, pp.41-50, 2008. ,
Efficient Belief Propagation for Vision Using Linear Constraint Nodes, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383094
Efficient belief propagation for higher-order cliques using linear constraint nodes, Computer Vision and Image Understanding, vol.112, issue.1, pp.39-54, 2008. ,
DOI : 10.1016/j.cviu.2008.05.007
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.4487
Some generalized order-disorder transitions, Proceedings of the Cambridge Philosophical Society, pp.106-109, 1952. ,
DOI : 10.1017/s0305004100027419
Conditional random fields for object recognition, Advances in Neural Information Processing Systems (NIPS), 2004. ,
A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989. ,
An MRF-based approach to generation of superresolution images from blurred observations, Journal of Mathematical Imaging and Vision, vol.16, issue.1, pp.5-15, 2002. ,
DOI : 10.1023/A:1013961817285
Exact inference in multi-label CRFs with higher order cliques, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587401
URL : https://hal.archives-ouvertes.fr/hal-01217304
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models, IEEE Transactions on Image Processing, vol.10, issue.7, pp.1056-1068, 2001. ,
DOI : 10.1109/83.931100
Reduction of bivalent maximization to the quadratic case, Cahiers du Centre d'etudes de Recherche Operationnelle, pp.71-74, 1975. ,
Fields of Experts: A Framework for Learning Image Priors, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005. ,
DOI : 10.1109/CVPR.2005.160
On the spatial statistics of optical flow, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.33-50, 2007. ,
DOI : 10.1109/ICCV.2005.180
Fields of Experts, International Journal of Computer Vision, vol.27, issue.2, pp.205-229, 2009. ,
DOI : 10.1007/s11263-008-0197-6
Minimizing sparse higher order energy functions of discrete variables, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206739
"GrabCut", ACM Transactions on Graphics, vol.23, issue.3, pp.309-314, 2004. ,
DOI : 10.1145/1015706.1015720
Optimizing Binary MRFs via Extended Roof Duality, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383203
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.4613
A maximum-flow formulation of the N-camera stereo correspondence problem, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998. ,
DOI : 10.1109/ICCV.1998.710763
MRF solutions for probabilistic optical flow formulations, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000. ,
DOI : 10.1109/ICPR.2000.903724
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.4458
Gaussian Markov Random Fields: Theory and Applications, 2005. ,
DOI : 10.1201/9780203492024
Learning in Markov random fields using tempered transitions, Advances in Neural Information Processing Systems (NIPS), 2009. ,
Transforming an arbitrary minsum problem into a binary one, 2006. ,
Minimal Shape and Intensity Cost Path Segmentation, IEEE Transactions on Medical Imaging, vol.26, issue.8, pp.1115-1129, 2007. ,
DOI : 10.1109/TMI.2007.896924
URL : https://lirias.kuleuven.be/bitstream/123456789/72327/1/Seghers07IEEETMI.pdf
The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams Efficient MRF deformation model for non-rigid image matching, Conference on Uncertainty in Artificial Intelligence (UAI), pp.91-99, 1998. ,
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, International Journal of Computer Vision, vol.62, issue.1???2, pp.2-23, 2009. ,
DOI : 10.1007/s11263-007-0109-1
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), 2006. ,
DOI : 10.1109/CVPR.2006.180
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.5114
Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation, Advances in Neural Information Processing Systems (NIPS), 2003. ,
DOI : 10.1007/s11263-011-0493-4
P-brush: Continuous valued MRFs with normed pairwise distributions for image segmentation, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206669
URL : http://cis.jhu.edu/%7Edheeraj/papers/cvpr09-pbrush.pdf
New outer bounds on the marginal polytope, Advances in Neural Information Processing Systems (NIPS), 2007. ,
A wearable computer based american sign language recognizer, Assistive Technology and Artificial Intelligence: Applications in Robotics, User Interfaces and Natural Language Processing, pp.84-96, 1998. ,
DOI : 10.1109/iswc.1997.629929
URL : http://c2000.cc.gatech.edu/classes/cs8113c_99_spring/readings/starner.pdf
Parallel and distributed graph cuts by dual decomposition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539886
Nonparametric belief propagation, Communications of the ACM, vol.53, issue.10, pp.95-103, 2010. ,
DOI : 10.1145/1831407.1831431
Distributed occlusion reasoning for tracking with nonparametric belief propagation, Advances in Neural Information Processing Systems (NIPS), 2004. ,
Visual Hand Tracking Using Nonparametric Belief Propagation, 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004. ,
DOI : 10.1109/CVPR.2004.474
Stereo Matching Using Belief Propagation, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.25, issue.7, pp.787-800, 2003. ,
DOI : 10.1007/3-540-47967-8_34
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2039
An Introduction to Conditional Random Fields. Foundations and Trends in Machine Learning, 2011. ,
Computer vision: algorithms and applications, 2010. ,
DOI : 10.1007/978-1-84882-935-0
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.6, pp.301068-1080, 2008. ,
DOI : 10.1109/TPAMI.2007.70844
Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters, IEEE International Conference on Computer Vision (ICCV), 2003. ,
HOP-MAP: Efficient Message Passing with High Order Potentials, International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. ,
Regularization of Inverse Visual Problems Involving Discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.4, pp.413-424, 1986. ,
DOI : 10.1109/TPAMI.1986.4767807
Tracking with Kalman snakes, Active vision, pp.3-20, 1993. ,
Solutions of ill-posed problems, 1977. ,
Feature Correspondence Via Graph Matching: Models and Global Optimization, European Conference on Computer Vision (ECCV), 2008. ,
DOI : 10.1007/978-3-540-88688-4_44
Detection of linear features in SAR images: application to road network extraction, IEEE Transactions on Geoscience and Remote Sensing, vol.36, issue.2, pp.434-453, 1998. ,
DOI : 10.1109/36.662728
Approximation Algorithms, 2001. ,
Star Shape Prior for Graph-Cut Image Segmentation, European Conference on Computer Vision (ECCV), 2008. ,
DOI : 10.1007/978-3-540-88690-7_34
Superpixels and Supervoxels in an Energy Optimization Framework, European Conference on Computer Vision (ECCV), 2010. ,
DOI : 10.1007/978-3-642-15555-0_16
Graph cut based image segmentation with connectivity priors, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587440
Joint optimization of segmentation and appearance models, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459287
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, pp.292241-2246, 2007. ,
DOI : 10.1109/TPAMI.2007.70712
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations, Statistics and Computing, vol.14, issue.2, pp.143-166, 2004. ,
DOI : 10.1023/B:STCO.0000021412.33763.d5
MAP Estimation Via Agreement on Trees: Message-Passing and Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.11, pp.513697-3717, 2005. ,
DOI : 10.1109/TIT.2005.856938
Graphical Models, Exponential Families, and Variational Inference, Machine Learning, pp.1-305, 2007. ,
Segmentation, ordering and multiobject tracking using graphical models, IEEE International Conference on Computer Vision, 2009. ,
3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs, International Conference, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2010. ,
DOI : 10.1007/978-3-642-15711-0_24
URL : https://hal.archives-ouvertes.fr/hal-00856051
Ioannis Kakadiaris, Dimitris Samaras, and Nikos Paragios. Viewpoint invariant 3d landmark model inference from monocular 2d images using higher-order priors, IEEE International Conference on Computer Vision (ICCV), 2011. ,
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs, IEEE Transactions on Information Theory, vol.47, issue.2, pp.736-744, 2001. ,
DOI : 10.1109/18.910585
A Linear Programming Approach to Max-Sum Problem: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1165-1179, 2007. ,
DOI : 10.1109/TPAMI.2007.1036
High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF), 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587355
Revisiting the Linear Programming Relaxation Approach to Gibbs Energy Minimization and Weighted Constraint Satisfaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1474-1488, 2010. ,
DOI : 10.1109/TPAMI.2009.134
Global Stereo Reconstruction under Second-Order Smoothness Priors, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), issue.12, pp.312115-2128, 2009. ,
Neural Decoding of Cursor Motion using a Kalman Filter, Advances in Neural Information Processing Systems (NIPS), 2002. ,
Tagged cardiac mr image segmentation using boundary & regional-support and graphbased deformable priors, IEEE International Symposium on Biomedical Imaging (ISBI), 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00856116
Linear Programming Relaxations and Belief Propagation-An Empirical Study, The Journal of Machine Learning Research, vol.7, pp.1887-1907, 2006. ,
Understanding Belief Propagation and its Generalizations, Exploring artificial intelligence in the new millennium, pp.239-269, 2003. ,
Dense non-rigid surface registration using high-order graph matching, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5540189
URL : https://hal.archives-ouvertes.fr/hal-00856064
Dimitris Samaras, and Nikos Paragios . A generic local deformation model for shape registration, 2011. ,
Dimitris Samaras, and Nikos Paragios . Intrinsic dense 3d surface tracking, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. ,
Active and dynamic information fusion for facial expression understanding from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.5, pp.699-714, 2005. ,
DOI : 10.1109/TPAMI.2005.93
Superpixels via pseudo-Boolean optimization, 2011 International Conference on Computer Vision, 2011. ,
DOI : 10.1109/ICCV.2011.6126393
Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.9, pp.884-900, 1996. ,
DOI : 10.1109/34.537343
Directed Graphical Models) ,
Undirected Graphical Models) ,
17 4.2.1 Belief Propagation in Tree, Junction Tree Algorithm ,
22 4.4.1 Order Reduction and Graph Cuts, p.23 ,