Modeling protein structure alignment based on Markov random field theory by Sheng, Xin.

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Efficiently solving dynamic Markov random fields using graph cuts by Kohli, P, Torr, PHS

A hidden Markov random field model for partial volume classification by Zhang, Y, Brady, M, Smith, S

High‐order Markov random field for single depth image super‐resolution by Elham Shabaninia, Ahmad Reza Naghsh‐Nilchi, Shohreh Kasaei

Subjects: “…high-order Markov random field…”
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ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD by F. J. Aguilar, M. A. Aguilar, J. L. Blanco, A. Nemmaoui, A. M. García Lorca

“…This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. …”
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Efficient and Scalable Approach to Equilibrium Conditional Simulation of Gibbs Markov Random Fields by Žukovič Milan, Hristopulos Dionissios T.

“…We study the performance of an automated hybrid Monte Carlo (HMC) approach for conditional simulation of a recently proposed, single-parameter Gibbs Markov random field. This is based on a modified version of the planar rotator (MPR) model and is used for efficient gap filling in gridded data. …”
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Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control by Sangkyun Lee, Piotr Sobczyk, Malgorzata Bogdan

Subjects: “…gaussian markov random field…”
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MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior by Marko Panić, Dušan Jakovetić, Dejan Vukobratović, Vladimir Crnojević, Aleksandra Pižurica

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High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition by Zhipeng Jiang, Chengwei Huang

Subjects: “…high-order markov random fields…”
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An Order Reduction Design Framework for Higher-Order Binary Markov Random Fields by Zhuo Chen, Hongyu Yang, Yanli Liu

Subjects: “…Markov random field…”
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SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL by J. Zhao, G. Huang, Z. Zhao

“…So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. …”
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Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria by Jih-Jeng Huang, Chin-Yi Chen

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A hidden Markov random field model for segmentation of brain MR images by Zhang, Y, Brady, M, Smith, S

Solving Markov random fields using second order cone programming relaxations by Kumar, MP, Torr, PHS, Zisserman, A

“…This paper presents a generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). …”

Constructing tissue-specific transcriptional regulatory networks via a Markov random field by Shining Ma, Tao Jiang, Rui Jiang

“…Results We propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. …”
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Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles by Pengyun Chen, Ying Liu, Xiaolong Chen, Teng Ma, Lei Zhang

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IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA by Jati Pratomo, Triyoga Widiastomo

Subjects: “…Markov Random Field…”
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Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials by Peixin Liu, Xiaofeng Li, Yang Wang, Zhizhong Fu

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De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting by Hannan Adeel, Muhammad Mohsin Riaz, Syed Sohaib Ali

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Hyperspectral image classification with deep 3D capsule network and Markov random field by Xiong Tan, Zhixiang Xue, Xuchu Yu, Yifan Sun, Kuiliang Gao

“…Abstract To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network and Markov random field. Based on this method, the deep spatial‐spectral features of hyperspectral images are extracted using the deep residual 3D convolutional structure, the vector capsules of the features are obtained by the initial capsule layer and mapped into probability capsules via the 3D dynamic routing mechanism to construct the classification probability map, and the spatial structure of the classification results is regularised by the Markov random field to further improve the classification accuracy and performance of the images. …”
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