An Order Reduction Design Framework for Higher-Order Binary Markov Random Fields [PDF]
Entropy, 2023 The order reduction method is an important approach to optimize higher-order binary Markov random fields (HoMRFs), which are widely used in information theory, machine learning and image analysis.
Zhuo Chen, Hongyu Yang, Yanli Liu
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Learning loosely connected Markov random fields
Stochastic Systems, 2014 We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm ...
Rui Wu, R. Srikant, Jian Ni
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Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields [PDF]
Frontiers in Neurology, 2019 Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions
Nagesh K. Subbanna +6 more
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Locally Adaptive Smoothing with Markov Random Fields and Shrinkage Priors. [PDF]
Bayesian Anal, 2018 We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combination of local ...
Faulkner JR, Minin VN.
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MRFalign: protein homology detection through alignment of Markov random fields. [PDF]
PLoS Computational Biology, 2014 Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein ...
Jianzhu Ma +3 more
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Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution [PDF]
Frontiers in Aging Neuroscience, 2017 18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors.
Fermín Segovia +5 more
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Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields. [PDF]
PLoS ONE, 2015 Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers.
Sean Robinson +5 more
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Restored texture segmentation using Markov random fields
Mathematical Biosciences and Engineering, 2023 Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process ...
Sanjaykumar Kinge +2 more
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Random Fields in Physics, Biology and Data Science
Frontiers in Physics, 2021 A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and ...
Enrique Hernández-Lemus +1 more
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Markov random fields model and applications to image processing
AIMS Mathematics, 2022 Markov random fields (MRFs) are well studied during the past 50 years. Their success are mainly due to their flexibility and to the fact that they gives raise to stochastic image models.
Boubaker Smii
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