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Journal articles on the topic 'Markov Random Field Model'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Paget, R. "Strong markov random field model." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 3 (2004): 408–13. http://dx.doi.org/10.1109/tpami.2004.1262338.

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2

Ming, Yansheng, and Zhanyi Hu. "Modeling Stereopsis via Markov Random Field." Neural Computation 22, no. 8 (2010): 2161–91. http://dx.doi.org/10.1162/neco_a_00005-ming.

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Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity e

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3

Chatzis, Sotirios P., and Gabriel Tsechpenakis. "The Infinite Hidden Markov Random Field Model." IEEE Transactions on Neural Networks 21, no. 6 (2010): 1004–14. http://dx.doi.org/10.1109/tnn.2010.2046910.

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4

Kent, John T., Kanti V. Mardia, and Alistair N. Walder. "Conditional cyclic Markov random fields." Advances in Applied Probability 28, no. 1 (1996): 1–12. http://dx.doi.org/10.2307/1427910.

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Grenander et al. (1991) proposed a conditional cyclic Gaussian Markov random field model for the edges of a closed outline in the plane. In this paper the model is recast as an improper cyclic Gaussian Markov random field for the vertices. The limiting behaviour of this model when the vertices become closely spaced is also described and in particular its relationship with the theory of ‘snakes' (Kass et al. 1987) is established. Applications are given in Grenander et al. (1991), Mardia et al. (1991) and Kent et al. (1992).

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Kent, John T., Kanti V. Mardia, and Alistair N. Walder. "Conditional cyclic Markov random fields." Advances in Applied Probability 28, no. 01 (1996): 1–12. http://dx.doi.org/10.1017/s0001867800027257.

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Grenander et al. (1991) proposed a conditional cyclic Gaussian Markov random field model for the edges of a closed outline in the plane. In this paper the model is recast as an improper cyclic Gaussian Markov random field for the vertices. The limiting behaviour of this model when the vertices become closely spaced is also described and in particular its relationship with the theory of ‘snakes' (Kass et al. 1987) is established. Applications are given in Grenander et al. (1991), Mardia et al. (1991) and Kent et al. (1992).

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6

Katakami, Shun, Hirotaka Sakamoto, Shin Murata, and Masato Okada. "Gaussian Markov Random Field Model without Boundary Conditions." Journal of the Physical Society of Japan 86, no. 6 (2017): 064801. http://dx.doi.org/10.7566/jpsj.86.064801.

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7

Ohno, Yoshinori, Kenji Nagata, Tatsu Kuwatani, Hayaru Shouno, and Masato Okada. "Deterministic Algorithm for Nonlinear Markov Random Field Model." Journal of the Physical Society of Japan 81, no. 6 (2012): 064006. http://dx.doi.org/10.1143/jpsj.81.064006.

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8

Cressie, Noel, and Subhash Lele. "New models for Markov random fields." Journal of Applied Probability 29, no. 4 (1992): 877–84. http://dx.doi.org/10.2307/3214720.

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The Hammersley–Clifford theorem gives the form that the joint probability density (or mass) function of a Markov random field must take. Its exponent must be a sum of functions of variables, where each function in the summand involves only those variables whose sites form a clique. From a statistical modeling point of view, it is important to establish the converse result, namely, to give the conditional probability specifications that yield a Markov random field. Besag (1974) addressed this question by developing a one-parameter exponential family of conditional probability models. In this ar

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9

Cressie, Noel, and Subhash Lele. "New models for Markov random fields." Journal of Applied Probability 29, no. 04 (1992): 877–84. http://dx.doi.org/10.1017/s0021900200043758.

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The Hammersley–Clifford theorem gives the form that the joint probability density (or mass) function of a Markov random field must take. Its exponent must be a sum of functions of variables, where each function in the summand involves only those variables whose sites form a clique. From a statistical modeling point of view, it is important to establish the converse result, namely, to give the conditional probability specifications that yield a Markov random field. Besag (1974) addressed this question by developing a one-parameter exponential family of conditional probability models. In this ar

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10

Halim, Siana. "APLIKASI MARKOV RANDOM FIELD PADA MASALAH INDUSTRI." Jurnal Teknik Industri 4, no. 1 (2004): 19–25. http://dx.doi.org/10.9744/jti.4.1.19-25.

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Markov chain in the stochastic process is widely used in the industrial problems particularly in the problem of determining the market share of products. In this paper we are going to extend the one in the random field so called the Markov Random Field and applied also in the market share problem with restriction the market is considered as a discrete lattice and Pott's models are going to be used as the potential function. Metropolis sampler is going to be used to determine the stability condition.    Abstract in Bahasa Indonesia :   Rantai Markov dalam proses st

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11

He, L., Z. Wu, Y. Zhang, and Z. Hu. "SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 75–81. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-75-2020.

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Abstract. 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 this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is

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12

Caorsi, S., G. L. Gragnani, S. Medicina, M. Pastorino, and G. Zunino. "Microwave imaging based on a Markov random field model." IEEE Transactions on Antennas and Propagation 42, no. 3 (1994): 293–303. http://dx.doi.org/10.1109/8.280714.

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13

Drabech, Zakariae, Mohammed Douimi, and Elmoukhtar Zemmouri. "A Markov random field model for change points detection." Journal of Computational Science 83 (December 2024): 102429. http://dx.doi.org/10.1016/j.jocs.2024.102429.

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14

Pardo-López, J. M., D. Cabello, J. Heras, and J. Couceiro. "A Markov random field model for bony tissue classification." Computerized Medical Imaging and Graphics 22, no. 2 (1998): 169–78. http://dx.doi.org/10.1016/s0895-6111(98)00018-4.

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15

Aggoun, L., L. Benkherouf, and A. Benmerzouga. "Optimal filters for a hidden Markov random field model." Mathematical and Computer Modelling 31, no. 13 (2000): 1–9. http://dx.doi.org/10.1016/s0895-7177(00)00107-2.

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VILLATORO, ESAÚ, ANTONIO JUÁREZ, MANUEL MONTES, LUIS VILLASEÑOR, and L. ENRIQUE SUCAR. "Document ranking refinement using a Markov random field model." Natural Language Engineering 18, no. 2 (2012): 155–85. http://dx.doi.org/10.1017/s1351324912000010.

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AbstractThis paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random

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17

Li, Min, and Truong Nguyen. "A De-Interlacing Algorithm Using Markov Random Field Model." IEEE Transactions on Image Processing 16, no. 11 (2007): 2633–48. http://dx.doi.org/10.1109/tip.2007.904967.

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18

Min Li and T. Q. Nguyen. "Markov Random Field Model-Based Edge-Directed Image Interpolation." IEEE Transactions on Image Processing 17, no. 7 (2008): 1121–28. http://dx.doi.org/10.1109/tip.2008.924289.

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19

Chen, Xiaohui, Chen Zheng, Hongtai Yao, and Bingxue Wang. "Image segmentation using a unified Markov random field model." IET Image Processing 11, no. 10 (2017): 860–69. http://dx.doi.org/10.1049/iet-ipr.2016.1070.

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20

Ahmadvand, Ali, and Peyman Kabiri. "Multispectral MRI image segmentation using Markov random field model." Signal, Image and Video Processing 10, no. 2 (2014): 251–58. http://dx.doi.org/10.1007/s11760-014-0734-4.

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21

Caputo, B. "A spin glass model of a Markov random field." International Journal of Imaging Systems and Technology 16, no. 5 (2006): 181–88. http://dx.doi.org/10.1002/ima.20086.

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22

Bhatt, M. R., and U. B. Desai. "Robust Image Restoration Algorithm Using Markov Random Field Model." CVGIP: Graphical Models and Image Processing 56, no. 1 (1994): 61–74. http://dx.doi.org/10.1006/cgip.1994.1006.

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23

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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24

Cai, Jinhai, and Zhi-Qiang Liu. "Pattern recognition using Markov random field models." Pattern Recognition 35, no. 3 (2002): 725–33. http://dx.doi.org/10.1016/s0031-3203(01)00071-1.

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25

Xiong, Si Chang, Ling Ping Dong, and Dong Hui Wen. "Tool Wear Image Segmentation Based on Markov Random Field Model." Advanced Materials Research 102-104 (March 2010): 600–604. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.600.

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In this article, we propose a tool wear image segmentation algorithm based on Markov Random Field model. In this algorithm, the wear area was divided into wear area, background area and body area, in terms of the MAP (maximum a posteriori) criterion, and we got the pre-segmentation image. Afterwards, the aiming area (region B) was segmented out from the wear area by using peak-band algorithm and the edges are integrated in mathematical morphology theorem. As a result, we obtained an integrated tool wear region. Experiments indicate that the segmentation algorithm can be used to evaluate the de

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26

Samant, Sunita, Pradipta Kumar Nanda, Ashish Ghosh, and Adya Kinkar Panda. "Noisy multimodal brain image registration using markov random field model." Biomedical Signal Processing and Control 73 (March 2022): 103426. http://dx.doi.org/10.1016/j.bspc.2021.103426.

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27

Cui, Yan Qiu, Tao Zhang, Shuang Xu, and Hou Jie Li. "Bayesian Image Denoising Using an Anisotropic Markov Random Field Model." Key Engineering Materials 467-469 (February 2011): 2018–23. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2018.

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This paper presents a Bayesian denoising method based on an anisotropic Markov Random Field (MRF) model in wavelet domain in order to improve the image denoising performance and reduce the computational complexity. The classical single-resolution image restoration method using MRFs and the maximum a posteriori (MAP) estimation is extended to the wavelet domain. To obtain the accurate MAP estimation, a novel anisotropic MRF model is proposed under this framework. As compared to the simple isotropic MRF model, this new model can capture the intrascale dependencies of wavelet coefficients signifi

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28

Modestino, J. W., and J. Zhang. "A Markov random field model-based approach to image interpretation." IEEE Transactions on Pattern Analysis and Machine Intelligence 14, no. 6 (1992): 606–15. http://dx.doi.org/10.1109/34.141552.

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29

Zhang, Yongyue, Michael Brady, and Stephen Smith. "A hidden markov random field model for partial volume classification." NeuroImage 13, no. 6 (2001): 291. http://dx.doi.org/10.1016/s1053-8119(01)91634-9.

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30

Hu, Runmei, and Moustafa M. Fahmy. "Texture segmentation based on a hierarchical Markov random field model." Signal Processing 26, no. 3 (1992): 285–305. http://dx.doi.org/10.1016/0165-1684(92)90117-f.

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31

Sun, Jian, Hongyan Zhu, Zongben Xu, and Chongzhao Han. "Poisson image fusion based on Markov random field fusion model." Information Fusion 14, no. 3 (2013): 241–54. http://dx.doi.org/10.1016/j.inffus.2012.07.003.

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32

Fang, Liangkun, Zhangjie Wu, Yuan Tao, and Jinfeng Gao. "Light Pollution Index System Model Based on Markov Random Field." Mathematics 11, no. 13 (2023): 3030. http://dx.doi.org/10.3390/math11133030.

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Light pollution is one of the environmental pollution problems facing the world. The research on the measurement standard of light pollution is not perfect at present. In this paper, we proposed a Markov random field model to determine the light pollution risk level of a site. Firstly, the specific data of 12 indicators of 5 typical cities were collected, and 10-factor indicators were screened using the R-type clustering algorithm. Then, the entropy weight method was used to determine the weight, and the light pollution measurement method of the Markov random field was established. The model w

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33

Alexander, Gómez-Villa, Díez-Valencia Germán, and Enrique Salazar-Jimenez Augusto. "A Markov random field image segmentation model for lizard spots." Revista Facultad de Ingeniería –redin-, no. 79 (June 16, 2016): 41–49. https://doi.org/10.17533/udea.redin.n79a05.

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Animal identification as a method for fauna study and conservation can be implemented using phenotypic appearance features such as spots, stripes or morphology. This procedure has the advantage that it does not harm study subjects. The visual identification of the subjects must be performed by a trained professional, who may need to inspect hundreds or thousands of images, a time-consuming task. In this work, several classical segmentation and preprocessing techniques, such as threshold, adaptive threshold, histogram equalization, and saturation correction are analyzed. Instead of the classica

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Zhang, Yingzhuo, Noa Malem-Shinitski, Stephen A. Allsop, Kay M. Tye, and Demba Ba. "Estimating a Separably Markov Random Field from Binary Observations." Neural Computation 30, no. 4 (2018): 1046–79. http://dx.doi.org/10.1162/neco_a_01059.

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A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to

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35

Lahouaoui, Lalaoui, and Djaalab Abdelhak. "Markov random field model and expectation of maximization for images segmentation." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 772. http://dx.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 EM Modified (EMM) model. In this paper, The local optimization is based on a modified Expectation-Maximization (EM) method for parameter estimation and the ICM method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry out a comparative study to fi

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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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37

Tsai, Meng-Hsiun, Yung-Kuan Chan, Jiun-Shiang Wang, Shu-Wei Guo, and Jiunn-Lin Wu. "Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model." Mathematical Problems in Engineering 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/410243.

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The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive t

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38

Willenborg, Leon. "The thrown string: a Markov field approach." Advances in Applied Probability 17, no. 3 (1985): 607–22. http://dx.doi.org/10.2307/1427122.

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39

Willenborg, Leon. "The thrown string: a Markov field approach." Advances in Applied Probability 17, no. 03 (1985): 607–22. http://dx.doi.org/10.1017/s0001867800015251.

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40

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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41

Ramanan, Kavita, Anirvan Sengupta, Ilze Ziedins, and Partha Mitra. "Markov random field models of multicasting in tree networks." Advances in Applied Probability 34, no. 1 (2002): 58–84. http://dx.doi.org/10.1239/aap/1019160950.

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In this paper, we analyse a model of a regular tree loss network that supports two types of calls: unicast calls that require unit capacity on a single link, and multicast calls that require unit capacity on every link emanating from a node. We study the behaviour of the distribution of calls in the core of a large network that has uniform unicast and multicast arrival rates. At sufficiently high multicast call arrival rates the network exhibits a ‘phase transition’, leading to unfairness due to spatial variation in the multicast blocking probabilities. We study the dependence of the phase tra

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42

Ramanan, Kavita, Anirvan Sengupta, Ilze Ziedins, and Partha Mitra. "Markov random field models of multicasting in tree networks." Advances in Applied Probability 34, no. 01 (2002): 58–84. http://dx.doi.org/10.1017/s0001867800011393.

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In this paper, we analyse a model of a regular tree loss network that supports two types of calls: unicast calls that require unit capacity on a single link, and multicast calls that require unit capacity on every link emanating from a node. We study the behaviour of the distribution of calls in the core of a large network that has uniform unicast and multicast arrival rates. At sufficiently high multicast call arrival rates the network exhibits a ‘phase transition’, leading to unfairness due to spatial variation in the multicast blocking probabilities. We study the dependence of the phase tra

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43

Forbes, F., and N. Peyrard. "Hidden markov random field model selection criteria based on mean field-like approximations." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 9 (2003): 1089–101. http://dx.doi.org/10.1109/tpami.2003.1227985.

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44

Manjunath, B. S., and R. Chellappa. "Unsupervised texture segmentation using Markov random field models." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 5 (1991): 478–82. http://dx.doi.org/10.1109/34.134046.

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Barker, S. A., and P. J. W. Rayner. "Unsupervised image segmentation using Markov random field models." Pattern Recognition 33, no. 4 (2000): 587–602. http://dx.doi.org/10.1016/s0031-3203(99)00074-6.

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46

Wang, Lei, and Jun Liu. "Texture classification using multiresolution Markov random field models." Pattern Recognition Letters 20, no. 2 (1999): 171–82. http://dx.doi.org/10.1016/s0167-8655(98)00129-9.

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47

De Bastiani, Fernanda, Robert A. Rigby, Dimitrios M. Stasinopoulous, Audrey H. M. A. Cysneiros, and Miguel A. Uribe-Opazo. "Gaussian Markov random field spatial models in GAMLSS." Journal of Applied Statistics 45, no. 1 (2016): 168–86. http://dx.doi.org/10.1080/02664763.2016.1269728.

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48

Nakashima, Yasuhisa, Yasuhiko Igarashi, Yasushi Naruse, and Masato Okada. "Robust One-dimensional Phase Unwrapping using a Markov Random Field Model." Journal of the Physical Society of Japan 87, no. 8 (2018): 084801. http://dx.doi.org/10.7566/jpsj.87.084801.

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49

Guo, Fan, Jin Tang, and Hui Peng. "A Markov Random Field Model for the Restoration of Foggy Images." International Journal of Advanced Robotic Systems 11, no. 6 (2014): 92. http://dx.doi.org/10.5772/58674.

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50

TANG, Hui-Xuan, and Hui WEI. "Figure-ground Separation by Contour Statistics and Markov Random Field Model." Acta Automatica Sinica 35, no. 8 (2009): 1033–40. http://dx.doi.org/10.3724/sp.j.1004.2009.01033.

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