Robust Regression-Based Markov Random Field for Hyperspectral Image Classification
IEEE Access, 2019 Recently, regression-based classifiers, such as the sparse representation classifier and collaborative representation classifier, have been proposed for hyperspectral image (HSI) classification.
Tianming Zhan +6 more
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Segmentasi Citra Kanker Serviks Menggunakan Markov Random Field dan Algoritma K-Means
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021 Cervical cancer is a dangerous disease caused by malignant tumors that grow on the cervix and has globally attacked many women. Pap smear test is one of the early prevention efforts for cervical cancer. Medical personnel often have difficulty identifying
Raihana Salsabila Darma Wijaya +3 more
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SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels.
J. Zhao, G. Huang, Z. Zhao
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Fuzzy Active Contour Model With Markov Random Field for Change Detection
IEEE Access, 2022 The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of ...
Fei Xie +5 more
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Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model
Mathematics, 2023 This work presents a Bayesian statistical approach to the saliency map estimation problem. More specifically, we formalize the saliency map estimation issue in the fully automatic Markovian framework.
Max Mignotte
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An Efficient Gaussian Filter Based on Gaussian Symmetric Markov Random Field
IEEE Access, 2022 This article presents a new image denoising algorithm that uses Gaussian Symmetric Markov random fields based on maximum a posteriori estimation. First, an image denoising model based on Gaussian Symmetric Markov random fields is built, and the image ...
Fusong Xiong +3 more
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Hierarchical non‐parametric Markov random field for image segmentation
IET Computer Vision, 2017 Markov random fields (MRFs) are prominent in modelling image to handle image processing problems. However, they confront the bottleneck of model selection in further improving the performance.
Xiangrong Wang, Jieyu Zhao
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Hidden Markov Random Field for Multi-Agent Optimal Decision in Top-Coal Caving
IEEE Access, 2020 Applying model-based learning for the optimal decision of the multi-agent system is not trivial due to the expensive price or even the impossibility of obtaining the ground truth for training the model of the complex environment.
Yi Yang +5 more
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Suggested Algorithm for Images Segmentation by Using Markov Random Field [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 In this research, Markov Random Fields Models have been used in images processing, included algorithm suggested for image segmentation tha depends on the triple mixture normal distribution.
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Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models [PDF]
, 2013 This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable ...
Batatia, Hadj +3 more
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