Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Markov random field modeling in computer vision
Markov random field modeling in computer vision
An efficient algorithm for image segmentation, Markov random fields and related problems
Journal of the ACM (JACM)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Applied Mathematics
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Mathematical Imaging and Vision
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Efficient belief propagation for higher-order cliques using linear constraint nodes
Computer Vision and Image Understanding
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows
International Journal of Computer Vision
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Parametric Maximum Flow Algorithms for Fast Total Variation Minimization
SIAM Journal on Scientific Computing
Efficient belief propagation with learned higher-order markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture synthesis via a noncausal nonparametric multiscale Markov random field
IEEE Transactions on Image Processing
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A feature of minimizing images of submodular binary Markov random field (MRF) energies is introduced. Using this novel feature, the collection of minimizing images of levels of higher order, monotonically levelable multilabel MRF energies is shown to constitute a monotone collection. This implies that these minimizing binary images can be combined to give minimizing images of the multilabel MRF energies. Thanks to the graph cuts framework, the mentioned class of binary MRF energies is known to be minimized by maximum flow computations on appropriately constructed graphs. With the aid of these developments an exact and efficient algorithm to minimize monotonically levelable multilabel MRF energies of any order, which is composed of a series of maximum flow computations, is proposed and an application of the proposed algorithm to image denoising is given.