Algorithms for clustering data
Algorithms for clustering data
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Gauss-Markov Measure Field Models for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Flexible Models Based on Robust Regularized Networks
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback
IEEE Transactions on Image Processing
A General Bayesian Markov Random Field Model for Probabilistic Image Segmentation
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
A variational approach for multi-valued velocity field estimation in transparent sequences
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Probabilistic rules for automatic texture segmentation
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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We present a computationally efficient segmentation–restoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the parameters of the feature generator models (restoration). Our algorithm computes an estimation of the posterior marginal probability distributions of the label field based on a Gauss Markov Random Measure Field model. Our proposal introduces an explicit entropy control for the estimated posterior marginals, therefore it improves the parameter estimation step. If the model parameters are given, our algorithm computes the posterior marginals as the global minimizers of a quadratic, linearly constrained energy function; therefore, one can compute very efficiently the optimal (Maximizer of the Posterior Marginals or MPM) estimator for multi–class segmentation problems. Moreover, a good estimation of the posterior marginals allows one to compute estimators different from the MPM for restoration problems, denoising and optical flow computation. Experiments demonstrate better performance over other state of the art segmentation approaches.