A new approach to the maximum-flow problem
Journal of the ACM (JACM)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A Convex Formulation of Continuous Multi-label Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
An initialization method for the K-Means algorithm using neighborhood model
Computers & Mathematics with Applications
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Graph cut optimization for the Mumford-Shah model
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
A note on the discrete binary Mumford-Shah model
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
IEEE Transactions on Knowledge and Data Engineering
Piecewise constant level set methods and image segmentation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Completely Convex Formulation of the Chan-Vese Image Segmentation Model
International Journal of Computer Vision
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
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
A Multiresolution Stochastic Level Set Method for Mumford–Shah Image Segmentation
IEEE Transactions on Image Processing
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The Mumford-Shah segmentation model is an energy model widely applied in computer vision. Many attempts have been made to minimize the energy of the model. We focus on recently proposed two methods for solving multi-phase segmentation; the graph cuts method by Bae and Tai (2009) [16] and the Monte Carlo method by Watanabe et al. (2011) [21]. We compare the convergence of solutions, the values of obtained energy, the computational time, etc. Finally we propose a hybrid method combining the advantages of the Monte Carlo and the graph cuts. The hybrid method can find the global minimum energy solution efficiently without sensitivity of initial guess.