Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Multi-label Moves for MRFs with Truncated Convex Priors
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
Solving Multilabel Graph Cut Problems with Multilabel Swap
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
A graph-cut based algorithm for approximate MRF optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fusion Moves for Markov Random Field Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Phase Unwrapping via Graph Cuts
IEEE Transactions on Image Processing
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This paper presents new graph-cut based optimization algorithms for image processing problems. Popular graph-cut based algorithms give approximate solutions and are based on the concept of partition move. The main contribution of this work consists in proposing novel partition moves called multilabel moves to minimize Markov random field (MRF) energies with convex prior and any likelihood energy functions. These moves improve the optimum quality of the state-of-the-art approximate minimization algorithms while controlling the memory need of the algorithm at the same time. Thus, the two challenging problems, improving local optimum quality and reducing required memory for graph construction are handled with our approach. These new performances are illustrated on some image processing experiments, such as image restoration and InSAR phase unwrapping.