Denoising Intra-voxel Axon Fiber Orientations by Means of ECQMMF Method
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A General Bayesian Markov Random Field Model for Probabilistic Image Segmentation
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Beta-measure for probabilistic segmentation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Optical flow estimation with prior models obtained from phase correlation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Alpha Markov Measure Field model for probabilistic image segmentation
Theoretical Computer Science
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
Optimal image restoration using HVS-based rate-distortion curves
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Segmentation of brain tissues using a 3-D multi-layer Hidden Markov Model
Computers in Biology and Medicine
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We present a new Markov random field (MRF) based model for parametric image segmentation. Instead of directly computing a label map, our method computes the probability that the observed data at each pixel is generated by a particular intensity model. Prior information about segmentation smoothness and low entropy of the probability distribution maps is codified in the form of a MRF with quadratic potentials so that the optimal estimator is obtained by solving a quadratic cost function with linear constraints. Although, for segmentation purposes, the mode of the probability distribution at each pixel is naturally used as an optimal estimator, our method permits the use of other estimators, such as the mean or the median, which may be more appropriate for certain applications. Numerical experiments and comparisons with other published schemes are performed, using both synthetic images and real data of brain MRI for which expert hand-made segmentations are available. Finally, we show that the proposed methodology may be easily extended to other problems, such as stereo disparity estimation.