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Oriented Triplet Markov Fields

  • Published:2018-02 Issue: Volume:103 Page:16-22
  • ISSN:0167-8655
  • Container-title:Pattern Recognition Letters
  • language:en
  • Short-container-title:Pattern Recognition Letters

Author:

Courbot Jean-Baptiste,Monfrini Emmanuel,Mazet Vincent,Collet Christophe

Funder

ERC

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images;Geman;IEEE Trans. Pattern Anal. Mach. Intell.,1984

2. On the statistical analysis of dirty pictures;Besag;J. Roy. Stat. Soc. B. Met.,1986

3. Probabilistic solution of ill-posed problems in computational vision;Marroquin;J. Am. Stat. Assoc.,1987

4. A Markov random field image segmentation model for color textured images;Kato;Image Vis. Comp.,2006

5. Fuzzy Markov random fields versus chains for multispectral image segmentation;Salzenstein;IEEE Trans. Pattern Anal. Mach. Intell.,2006

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1. Unsupervised statistical image segmentation using bi-dimensional hidden Markov chains model with application to mammography images;Journal of King Saud University - Computer and Information Sciences;2023-10

2. Unsupervised Image Segmentation with Spatial Triplet Markov Trees;ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2021-06-06

3. Unsupervised image segmentation with Gaussian Pairwise Markov Fields;Computational Statistics & Data Analysis;2021-06

4. Adaptive volumetric texture segmentation based on Gaussian Markov random fields features;Pattern Recognition Letters;2020-12

5. Estimation of Viterbi path in Bayesian hidden Markov models;METRON;2019-05-28
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