• Li L and Jiang Y. A Modified Fuzzy Markov Random Field Incorporating Multiple Features for Liver Tumor Segmentation. Artificial Intelligence. (352-363).

    https://doi.org/10.1007/978-981-99-8850-1_29

  • Filali H and Kalti K. (2021). Image segmentation using MRF model optimized by a hybrid ACO-ICM algorithm. Soft Computing - A Fusion of Foundations, Methodologies and Applications. 25:15. (10181-10204). Online publication date: 1-Aug-2021.

    https://doi.org/10.1007/s00500-021-05957-1

  • Touati R, Mignotte M and Dahmane M. (2019). Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise-Based Markov Random Field Model. IEEE Transactions on Image Processing. 29. (757-767). Online publication date: 1-Jan-2020.

    https://doi.org/10.1109/TIP.2019.2933747

  • Yang R, Qian X and Zhang B. Multi-Feature Fusion Aerial Image Segmentation in Complex Background. Proceedings of the 3rd International Conference on Vision, Image and Signal Processing. (1-8).

    https://doi.org/10.1145/3387168.3387237

  • Jain N and Kumar V. (2016). IFCM Based Segmentation Method for Liver Ultrasound Images. Journal of Medical Systems. 40:11. (1-12). Online publication date: 1-Nov-2016.

    https://doi.org/10.1007/s10916-016-0623-1

  • Zhang P, Li M, Wu Y, An L and Jia L. (2016). Unsupervised SAR image segmentation using high-order conditional random fields model based on product-of-experts. Pattern Recognition Letters. 78:C. (48-55). Online publication date: 15-Jul-2016.

    https://doi.org/10.1016/j.patrec.2016.03.032

  • Peng R and Varshney P. (2015). On performance limits of image segmentation algorithms. Computer Vision and Image Understanding. 132:C. (24-38). Online publication date: 1-Mar-2015.

    https://doi.org/10.1016/j.cviu.2014.11.004

  • Crivelli T, Bouthemy P, Cernuschi-Frías B and Yao J. (2011). Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field. International Journal of Computer Vision. 94:3. (295-316). Online publication date: 1-Sep-2011.

    https://doi.org/10.1007/s11263-011-0429-z

  • Salzenstein F and Collet C. (2006). Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28:11. (1753-1767). Online publication date: 1-Nov-2006.

    https://doi.org/10.1109/TPAMI.2006.228

  • Destrempes F and Mignotte M. (2004). A Statistical Model for Contours in Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26:5. (626-638). Online publication date: 1-May-2004.

    https://doi.org/10.1109/TPAMI.2004.1273940

  • Provost J, Collet C, Rostaing P, Pérez P and Bouthemy P. (2004). Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps. Computer Vision and Image Understanding. 93:2. (155-174). Online publication date: 1-Feb-2004.

    https://doi.org/10.1016/j.cviu.2003.07.004

  • Ibáñez M and Simó A. (2003). Parameter estimation in Markov random field image modeling with imperfect observations. Pattern Recognition Letters. 24:14. (2377-2389). Online publication date: 1-Oct-2003.

    https://doi.org/10.1016/S0167-8655(03)00067-9

  • Ruan S, Moretti B, Fadili J and Bloyet D. (2002). Fuzzy Markovian segmentation in application of magnetic resonance images. Computer Vision and Image Understanding. 85:1. (54-69). Online publication date: 1-Jan-2002.

    https://doi.org/10.1006/cviu.2002.0957

  • Mignotte M, Collet C, Pérez P and Bouthemy P. (2000). Markov Random Field and Fuzzy Logic Modeling in Sonar Imagery. Computer Vision and Image Understanding. 79:1. (4-24). Online publication date: 1-Jul-2000.

    https://doi.org/10.1006/cviu.2000.0844

  • Rosenfeld A. (1998). Image Analysis and Computer Vision. Computer Vision and Image Understanding. 70:2. (239-284). Online publication date: 1-May-1998.

    https://doi.org/10.1006/cviu.1998.0697