What Energy Functions Can Be Minimizedvia Graph Cuts?
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An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
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A Surface Reconstruction Method Using Global Graph Cut Optimization
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Graph Cuts and Efficient N-D Image Segmentation
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Convergent Tree-Reweighted Message Passing for Energy Minimization
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Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
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On partial optimality in multi-label MRFs
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Measuring uncertainty in graph cut solutions
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Robust Higher Order Potentials for Enforcing Label Consistency
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Implicit Active-Contouring with MRF
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Multi-label Moves for MRFs with Truncated Convex Priors
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SAR image regularization with fast approximate discrete minimization
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Multi-label MRF Optimization via a Least Squares s - t Cut
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Total absolute Gaussian curvature for stereo prior
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Exact solution of permuted submodular minsum problems
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Efficient shape matching via graph cuts
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Global optimization for first order Markov random fields with submodular priors
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
A graph-cut based algorithm for approximate MRF optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multi-label Depth Estimation for Graph Cuts Stereo Problems
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Stereo depth estimation using synchronous optimization with segment based regularization
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Fast and exact primal-dual iterations for variational problems in computer vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Convex relaxation for multilabel problems with product label spaces
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Minimization of monotonically levelable higher order MRF energies via graph cuts
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Graph search with appearance and shape information for 3-D prostate and bladder segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
An efficient graph cut algorithm for computer vision problems
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Improved Moves for Truncated Convex Models
The Journal of Machine Learning Research
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
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3D Graph cut with new edge weights for cerebral white matter segmentation
Pattern Recognition Letters
Optimal graph based segmentation using flow lines with application to airway wall segmentation
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A fast solver for truncated-convex priors: quantized-convex split moves
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Discrete optimization of the multiphase piecewise constant mumford-shah functional
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A Spatial Regularization Approach for Vector Quantization
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Compression and denoising using l0-norm
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SIAM Journal on Imaging Sciences
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A multiple graph cut based approach for stereo analysis
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Exact optimization of discrete constrained total variation minimization problems
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Statistical priors for efficient combinatorial optimization via graph cuts
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Total variation minimization and a class of binary MRF models
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Discontinuity preserving phase unwrapping using graph cuts
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Comparison of energy minimization algorithms for highly connected graphs
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A comparative study of energy minimization methods for markov random fields
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Efficient minimization of the non-local potts model
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Multi-label Moves for MRFs with Truncated Convex Priors
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Completely Convex Formulation of the Chan-Vese Image Segmentation Model
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Dynamic programming for approximate expansion algorithm
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Generalized roof duality for multi-label optimization: optimal lower bounds and persistency
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Nonmetric priors for continuous multilabel optimization
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Fast tiered labeling with topological priors
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Approximate envelope minimization for curvature regularity
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Review article: Multilabel partition moves for MRF optimization
Image and Vision Computing
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Remote sensing image object extraction using convex geometric active contour model
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Bayesian segmentation of atrium wall using globally-optimal graph cuts on 3D meshes
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Computer Vision and Image Understanding
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We introduce a method to solve exactly a first order Markov random field optimization problem in more generality than was previously possible. The MRF has a prior term that is convex in terms of a linearly ordered label set. The method maps the problem into a minimum-cut problem for a directed graph, for which a globally optimal solution can be found in polynomial time. The convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method.