Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Limited view CT reconstruction and segmentation via constrained metric labeling
Computer Vision and Image Understanding
Measuring uncertainty in graph cut solutions
Computer Vision and Image Understanding
On fusion of range and intensity information using Graph-Cut for planar patch segmentation
International Journal of Intelligent Systems Technologies and Applications
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Note: The expressive power of binary submodular functions
Discrete Applied Mathematics
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Graph cut optimization for the Mumford-Shah model
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Graph cuts with many-pixel interactions: Theory and applications to shape modelling
Image and Vision Computing
Minimization of monotonically levelable higher order MRF energies via graph cuts
IEEE Transactions on Image Processing
Minimizing count-based high order terms in markov random fields
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Higher order Markov networks for model estimation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
A multiple graph cut based approach for stereo analysis
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Discontinuity preserving phase unwrapping using graph cuts
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Minimizing a sum of submodular functions
Discrete Applied Mathematics
Discrete Applied Mathematics
Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Tighter relaxations for higher-order models based on generalized roof duality
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior
Journal of Mathematical Imaging and Vision
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The recent explosion of interest in graph cut methods in computer vision naturally spawns the question: what energy functions can be minimized via graph cuts? This question was first attacked by two papers of Kolmogorov and Zabih [23, 24], in which they dealt with functions with pairwise and triplewise pixel interactions. In this work, we extend their results in two directions. First, we examine the case of k-wise pixel interactions; the results are derived from a purely algebraic approach. Second, we discuss the applicability of provably approximate algorithms. Both of these developments should help researchers best understand what can and cannot be achieved when designing graph cut based algorithms.