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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SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to
L. He, Z. Wu, Y. Zhang, Z. Hu
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Context-Aware Deep Markov Random Fields for Fake News Detection
IEEE Access, 2021 Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news ...
Tien Huu Do +4 more
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One-dimensional Markov random fields, Markov chains and topological Markov fields [PDF]
Proceedings of the American Mathematical Society, 2013 15 ...
Marcus, B +4 more
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A Methodology for Redesigning Networks by Using Markov Random Fields
Mathematics, 2021 Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications.
Julia García Cabello +4 more
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On a Class of Tensor Markov Fields
Entropy, 2020 Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables.
Enrique Hernández-Lemus
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SAR Image Classification Using Markov Random Fields with Deep Learning
Remote Sensing, 2023 Classification algorithms integrated with convolutional neural networks (CNN) display high accuracies in synthetic aperture radar (SAR) image classification.
Xiangyu Yang +3 more
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Hyperspectral Image Classification With CapsNet and Markov Random Fields
IEEE Access, 2020 Hyperspectral image (HSI) classification is one of the most challenging problems in understanding HSI. Convolutional neural network(CNN), with the strong ability to extract features using the hidden layers in the network, has been introduced to solve ...
Xuefeng Jiang +6 more
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Inference Tools for Markov Random Fields on Lattices: The R Package mrf2d
Journal of Statistical Software, 2022 Markov random fields on two-dimensional lattices are behind many image analysis methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many of ...
Victor Freguglia, Nancy Lopes Garcia
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Uncertainty Quantification for Markov Random Fields [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2021 We present an information-based uncertainty quantification method for general Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic graphical models over undirected graphs, and provide a fundamental unifying modeling tool for statistical mechanics, probabilistic machine learning, and artificial intelligence.
Panagiota Birmpa, Markos A. Katsoulakis
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