Relevant bibliographies by topics / MRF, Markov Random Fields

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'MRF, Markov Random Fields'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "MRF, Markov Random Fields"

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Zhipeng, Jiang, and Huang Chengwei. "High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition." Cybernetics and Information Technologies 15, no. 4 (2015): 50–57. http://dx.doi.org/10.1515/cait-2015-0054.

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Abstract In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum, mean and standard deviation. Second, we adopt the high-order Markov random fields (MRF) to optimize the cross-language speech emotion model. The dimensional restrictions may be modeled by MRF.
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Cai, Kuntai, Xiaoyu Lei, Jianxin Wei, and Xiaokui Xiao. "Data synthesis via differentially private markov random fields." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2190–202. http://dx.doi.org/10.14778/3476249.3476272.

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This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency,
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Lee, Sang Heon, Adel Malallah, Akhil Datta-Gupta, and David Higdon. "Multiscale Data Integration Using Markov Random Fields." SPE Reservoir Evaluation & Engineering 5, no. 01 (2002): 68–78. http://dx.doi.org/10.2118/76905-pa.

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Summary We propose a hierarchical approach to spatial modeling based on Markov Random Fields (MRF) and multiresolution algorithms in image analysis. Unlike their geostatistical counterparts, which simultaneously specify distributions across the entire field, MRFs are based on a collection of full conditional distributions that rely on the local neighborhoods of each element. This critical focus on local specification provides several advantages:MRFs are computationally tractable and are ideally suited to simulation based computation, such as Markov Chain Monte Carlo (MCMC) methods, andmodel ex
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Yang, Xiangyu, Xuezhi Yang, Chunju Zhang, and Jun Wang. "SAR Image Classification Using Markov Random Fields with Deep Learning." Remote Sensing 15, no. 3 (2023): 617. http://dx.doi.org/10.3390/rs15030617.

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Classification algorithms integrated with convolutional neural networks (CNN) display high accuracies in synthetic aperture radar (SAR) image classification. However, their consideration of spatial information is not comprehensive and effective, which causes poor performance in edges and complex regions. This paper proposes a Markov random field (MRF)-based algorithm for SAR image classification which fully considers the spatial constraints between superpixel regions. Firstly, the initialization of region labels is obtained by the CNN. Secondly, a probability field is constructed to improve th
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Jin, Di, Ziyang Liu, Weihao Li, Dongxiao He, and Weixiong Zhang. "Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 152–59. http://dx.doi.org/10.1609/aaai.v33i01.3301152.

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Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields (MRF). Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information. Our new method takes advantage of salient features of GNN and MRF and exploits both network topology a
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Smii, Boubaker. "Markov random fields model and applications to image processing." AIMS Mathematics 7, no. 3 (2022): 4459–71. http://dx.doi.org/10.3934/math.2022248.

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<abstract><p>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. In this work, we will consider a stochastic differential equation (SDE) driven by Lévy noise. We will show that the solution $ X_v $ of the SDE is a MRF satisfying the Markov property. We will prove that the Gibbs distribution of the process $ X_v $ can be represented graphically through Feynman graphs, which are defined as a set of cliques, then we will provide applications of MRFs in
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Kurella, Pushpak. "Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection." International Transactions on Electrical Engineering and Computer Science 2, no. 4 (2023): 183–90. http://dx.doi.org/10.62760/iteecs.2.4.2023.68.

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The brain tissues segmented by MRI and CT provide a more accurate viewpoint on diagnosing various brain illnesses. Many different segmentation approaches may be used to brain MRI images. Some of the most successful include Histogram thresholding, area based segmentation (K-means, Expectation and Maximization (EM), Fuzzy connectivity, and Markov random fields (MRF). The Hidden Markov Random field (HMRF) approach is one of the most effective segmentation techniques available. It is capable of solving quickly distinct brain tissues for recognition purposes. Using the HMRF model allows for the red
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Shi, Haoran, Lixin Ji, Shuxin Liu, Kai Wang, and Xinxin Hu. "Collusive anomalies detection based on collaborative markov random field." Intelligent Data Analysis 26, no. 6 (2022): 1469–85. http://dx.doi.org/10.3233/ida-216287.

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Abnormal collusive behavior, widely existing in various fields with concealment and synergy, is particularly harmful in user-generated online reviews and hard to detect by traditional methods. With the development of network science, this problem can be solved by analyzing structure features. As a graph-based anomaly detection method, the Markov random field (MRF)-based model has been widely used to identify the collusive anomalies and shown its effectiveness. However, existing methods are mostly unable to highlight the primary synergy relationship among nodes and consider much irrelevant info
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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.

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<abstract> <p>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 performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a hug
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Lalaoui, Lahouaoui, and Abdelhak Djaalab. "Markov random field model and expectation of maximization for images segmentation." Markov random field model and expectation of maximization for images segmentation 29, no. 2 (2023): 772–79. https://doi.org/10.11591/ijeecs.v29.i2.pp772-779.

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Image segmentation is a significant issue in image processing. Among the various models and approaches that have been developed, some are commonly used the Markov random field (MRF) model, statistical techniques MRF. In this study a Markov random field proposed is based on an expectation-maximization (EM) modified (EMM) model. In this paper, the local optimization is based on a modified EM method for parameter estimation and the iterative conditional model (ICM) method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry o
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Dissertations / Theses on the topic "MRF, Markov Random Fields"

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Samuel, Kegan. "Gradient based MRF learning for image restoration and segmentation." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5480.

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The undirected graphical model or Markov Random Field (MRF) is one of the more popular models used in computer vision and is the type of model with which this work is concerned. Models based on these methods have proven to be particularly useful in low-level vision systems and have led to state-of-the-art results for MRF-based systems. The research presented will describe a new discriminative training algorithm and its implementation. The MRF model will be trained by optimizing its parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth.
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Kato, Jien, Toyohide Watanabe, Sébastien Joga, et al. "An HMM/MRF-based stochastic framework for robust vehicle tracking." IEEE, 2004. http://hdl.handle.net/2237/6743.

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Karci, Mehmet Haydar. "Higher Order Levelable Mrf Energy Minimization Via Graph Cuts." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609408/index.pdf.

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A feature of minimizing images of a class of binary Markov random field energies is introduced and proved. Using this, the collection of minimizing images of levels of higher order, levelable MRF energies is shown to be a monotone collection. This implies that these images can be combined to give minimizing images of the MRF energy itself. Due to the recent developments, second and third order binary MRF energies of the mentioned class are known to be exactly minimized by maximum flow/minimum cut computations on appropriately constructed graphs. With the aid of these developments an exact and
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Gasnier, Nicolas. "Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT002.

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La télédétection spatiale fournit aux hydrologues et aux décideurs des données indispensables à la compréhension du cycle de l’eau et à la gestion des ressources et risques associés. Le satellite SWOT, qui est une collaboration entre les agences spatiales françaises (CNES) et américaine (NASA, JPL), et dont le lancement est prévu en 2022 vise à mesurer la hauteur des lacs, rivières et océans avec une grande résolution spatiale. Il complétera ainsi les capteurs existants, comme les constellations SAR et optique Sentinel-1 et 2 et les relevés in situ. SWOT représente une rupture technologique ca
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Besbes, Ahmed. "Image segmentation using MRFs and statistical shape modeling." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00594246.

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Nous présentons dans cette thèse un nouveau modèle statistique de forme et l'utilisons pour la segmentation d'images avec a priori. Ce modèle est représenté par un champ de Markov. Les noeuds du graphe correspondent aux points de contrôle situés sur le contour de la forme géométrique, et les arêtes du graphe représentent les dépendances entre les points de contrôle. La structure du champ de Markov est déterminée à partir d'un ensemble de formes, en utilisant des techniques d'apprentissage de variétés et de groupement non-supervisé. Les contraintes entre les points sont assurées par l'estimatio
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Kale, Hikmet Emre. "Segmentation Of Human Facial Muscles On Ct And Mri Data Using Level Set And Bayesian Methods." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613352/index.pdf.

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Medical image segmentation is a challenging problem, and is studied widely. In this thesis, the main goal is to develop automatic segmentation techniques of human mimic muscles and to compare them with ground truth data in order to determine the method that provides best segmentation results. The segmentation methods are based on Bayesian with Markov Random Field (MRF) and Level Set (Active Contour) models. Proposed segmentation methods are multi step processes including preprocess, main muscle segmentation step and post process, and are applied on three types of data: Magnetic Resonance Imagi
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Wang, Siying. "Segmentation of magnetic resonance images for assessing neonatal brain maturation." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:96db1546-16c1-4e37-9fd2-6431b385b516.

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In this thesis, we aim to investigate the correlation between myelination and the gestational age for preterm infants, with the former being an important developmental process during human brain maturation. Quantification of myelin requires dedicated imaging, but the conventional magnetic resonance images routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. This thesis thus focuses on structural segmentation and spatio-temporal modelling of the so-called myelin-like signals on T2-weighted scans for early prognostic evaluati
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Stien, Marita. "Sequential Markov random fields and Markov mesh random fields for modelling of geological structures." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9326.

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<p>We have been given a two-dimensional image of a geological structure. This structure is used to construct a three-dimensional statistical model, to be used as prior knowledge in the analysis of seismic data. We consider two classes of discrete lattice models for which efficient simulation is possible; sequential Markov random field (sMRF) and Markov mesh random field (MMRF). We first explore models from these two classes in two dimensions, using the maximum likelihood estimator (MLE). The results indicate that a larger neighbourhood should be considered for all the models. We also develop a
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Austad, Haakon Michael. "Approximations of Binary Markov Random Fields." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14922.

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Drouin, Simon. "Digital rotoscoping using Markov random fields." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32535.

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This thesis presents a statistical framework and its implementation in a user-assisted rotoscoping program intended for the production of animation movies. User-assisted video segmentation of scenes with well-defined foreground and background, a special case of the general problem of rotoscoping, is used to analyze the properties of the framework and its implementation. The statistical model used in the framework is built from pairs of training images composed of a frame from the sequence to segment and o
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Books on the topic "MRF, Markov Random Fields"

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Rama, Chellappa, and Jain Anil K. 1948-, eds. Markov random fields: Theory and application. Academic Press, 1993.

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Snell, J. Laurie (James Laurie), 1925-2011, ed. Markov random fields and their applications. AMS, 2003.

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Andrew, Blake, Pushmeet Kohli, and Carsten Rother. Markov random fields for vision and image processing. MIT Press, 2011.

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Li, S. Z. Markov random field modeling in computer vision. Springer-Verlag, 1995.

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Xu, Jinbo, Sheng Wang, and Jianzhu Ma. Protein Homology Detection Through Alignment of Markov Random Fields. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14914-1.

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Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6.

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Gimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Springer Netherlands, 1999.

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Li, S. Z. Markov random field modeling in image analysis. 3rd ed. Springer, 2009.

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Gerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Springer, 2003.

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1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Springer, 2003.

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Book chapters on the topic "MRF, Markov Random Fields"

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Shekhar, Shashi, and Hui Xiong. "Markov Random Field (MRF)." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_758.

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Li, S. Z. "MRF Parameter Estimation." In Markov Random Field Modeling in Computer Vision. Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_6.

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Wang, Zifu, and Matthew B. Blaschko. "MRF-UNets: Searching UNet with Markov Random Fields." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_36.

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Li, S. Z. "Low Level MRF Models." In Markov Random Field Modeling in Computer Vision. Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_2.

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Li, S. Z. "High Level MRF Models." In Markov Random Field Modeling in Computer Vision. Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_5.

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Nakamura, Rodrigo, Daniel Osaku, Alexandre Levada, Fabio Cappabianco, Alexandre Falcão, and Joao Papa. "OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_29.

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Sucar, Luis Enrique. "Markov Random Fields." In Probabilistic Graphical Models. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_6.

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Mitchell, H. B. "Markov Random Fields." In Image Fusion. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_17.

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Fieguth, Paul. "Markov Random Fields." In Statistical Image Processing and Multidimensional Modeling. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7294-1_6.

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Guttorp, Peter. "Markov random fields." In Stochastic Modeling of Scientific Data. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4449-8_4.

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Conference papers on the topic "MRF, Markov Random Fields"

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Grover, Ishaan, Matthew Huggins, Cynthia Breazeal, and Hae Won Park. "MRF-Chat: Improving Dialogue with Markov Random Fields." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.403.

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Guo, Jinnian, Xinyu Wu, Tian Cao, Shiqi Yu, and Yangsheng Xu. "Crowd density estimation via Markov Random Field (MRF)." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554998.

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Wu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.

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Markov random fields (MRFs) [1, 2, 3, 4] provide attractive statistical models for multidimensional signals. However, unfortunately, optimal Bayesian estimators tend to require large amounts of computation. We present an approximation to a particular Bayesian estimator which requires much reduced computation and an example illustrating low-light unknown-blur imaging. See [7] for an alternative approximation based on approximating the MRF lattice by a system of trees and for an alternative cost function.
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Kusuma, T., and S. Jagannathn. "Review on Markov Random Field (Mrf) in Video Surveillance." In Third International Conference on Current Trends in Engineering Science and Technology ICCTEST-2017. Grenze Scientific Society, 2017. http://dx.doi.org/10.21647/icctest/2017/49071.

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Zhang, Yue, and Arti Ramesh. "Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/838.

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Statistical relational models such as Markov logic networks (MLNs) and hinge-loss Markov random fields (HL-MRFs) are specified using templated weighted first-order logic clauses, leading to the creation of complex, yet easy to encode models that effectively combine uncertainty and logic. Learning the structure of these models from data reduces the human effort of identifying the right structures. In this work, we present an asynchronous deep reinforcement learning algorithm to automatically learn HL-MRF clause structures. Our algorithm possesses the ability to learn semantically meaningful str
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Dong, Yiqi, Dongxiao He, Xiaobao Wang, Yawen Li, Xiaowen Su, and Di Jin. "A Generalized Deep Markov Random Fields Framework for Fake News Detection." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/529.

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Recently, the wanton dissemination of fake news on social media has adversely affected our lives, rendering automatic fake news detection a pressing issue. Current methods are often fully supervised and typically employ deep neural networks (DNN) to learn implicit relevance from labeled data, ignoring explicitly shared properties (e.g., inflammatory expressions) across fake news. To address this limitation, we propose a graph-theoretic framework, called Generalized Deep Markov Random Fields Framework (GDMRFF), that inherits the capability of deep learning while at the same time exploiting the
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Lei, Tianhu, and Jayaram K. Udupa. "A new look at Markov random field (MRF) model-based MR image analysis." In Medical Imaging, edited by J. Michael Fitzpatrick and Joseph M. Reinhardt. SPIE, 2005. http://dx.doi.org/10.1117/12.596251.

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Xiong, Hao, and Nicholas Ruozzi. "General Purpose MRF Learning with Neural Network Potentials." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/384.

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Maximum likelihood learning is a well-studied approach for fitting discrete Markov random fields (MRFs) to data. However, general purpose maximum likelihood estimation for fitting MRFs with continuous variables have only been studied in much more limited settings. In this work, we propose a generic MLE estimation procedure for MRFs whose potential functions are modeled by neural networks. To make learning effective in practice, we show how to leverage a highly parallelizable variational inference method that can easily fit into popular machining learning frameworks like TensorFlow. We demonstr
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Samy, Roger A., and Daniel Duclos. "Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Nagaraj Nandhakumar. SPIE, 1994. http://dx.doi.org/10.1117/12.179033.

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Lin, Jiawei, and Sei-Ichiro Kamata. "Using Markov Random Field (MRF) Hypergraph Transformer Method for Visual Question Answering (VQA) Application." In 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2023. http://dx.doi.org/10.1109/prai59366.2023.10332038.

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Reports on the topic "MRF, Markov Random Fields"

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Luettgen, M. R., W. C. Karl, A. S. Willsky, and R. R. Tenney. Multiscale Representations of Markov Random Fields. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada459389.

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Luettgen, Mark R., William C. Karl, Alan S. Willsky, and Robert R. Tenney. Multiscale Representations of Markov Random Fields. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada459967.

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Cevher, Volkan, Chinmay Hegde, Marco F. Duarte, and Richard G. Baraniuk. Sparse Signal Recovery Using Markov Random Fields. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada520187.

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Mitter, Sanjoy K. Markov Random Fields, Stochastic Quantization and Image Analysis. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada459566.

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Anandkumar, Animashree, Lang Tong, and Ananthram Swami. Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada536158.

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Adler, Robert J., and R. Epstein. A Central Limit Theorem for Markov Paths and Some Properties of Gaussian Random Fields. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170258.

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