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

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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Qi, Anna, Lihua Yang, and Chao Huang. "Convergence of Markovian stochastic approximation for Markov random fields with hidden variables." Stochastics and Dynamics 20, no. 05 (2019): 2050029. http://dx.doi.org/10.1142/s021949372050029x.

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This paper studies the convergence of the stochastic algorithm of the modified Robbins–Monro form for a Markov random field (MRF), in which some of the nodes are clamped to be observed variables while the others are hidden ones. Based on the theory of stochastic approximation, we propose proper assumptions to guarantee the Hölder regularity of both the update function and the solution of the Poisson equation. Under these assumptions, it is proved that the control parameter sequence is almost surely bounded and accordingly the algorithm converges to the stable point of the log-likelihood functi

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Liu, Lu, and Yongxiang Li. "PolSAR Image Classification with Active Complex-Valued Convolutional-Wavelet Neural Network and Markov Random Fields." Remote Sensing 16, no. 6 (2024): 1094. http://dx.doi.org/10.3390/rs16061094.

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PolSAR image classification has attracted extensive significant research in recent decades. Aiming at improving PolSAR classification performance with speckle noise, this paper proposes an active complex-valued convolutional-wavelet neural network by incorporating dual-tree complex wavelet transform (DT-CWT) and Markov random field (MRF). In this approach, DT-CWT is introduced into the complex-valued convolutional neural network to suppress the speckle noise of PolSAR images and maintain the structures of learned feature maps. In addition, by applying active learning (AL), we iteratively selec

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Saon, George, and Abdel Belaïd. "High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Fields." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 05 (1997): 771–88. http://dx.doi.org/10.1142/s0218001497000342.

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In this paper we present a system for the recognition of handwritten words on literal check amounts which advantageously combine HMMs and Markov random fields (MRFs). It operates at pixel level, in a holistic manner, on height normalized word images which are viewed as random field realizations. The HMM analyzes the image along the horizontal writing direction, in a specific state observation probability given by the column product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the pape

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Yao, Hongtai, Xianpei Wang, Le Zhao, et al. "An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image." Remote Sensing 14, no. 1 (2021): 127. http://dx.doi.org/10.3390/rs14010127.

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The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be re

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Shu, Zhen, Kai Sun, Kaijin Qiu, and Kou Ding. "PAIRWISE-SVM FOR ON-BOARD URBAN ROAD LIDAR CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 109–13. http://dx.doi.org/10.5194/isprsarchives-xli-b1-109-2016.

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The common method of LiDAR classifications is Markov random fields (MRF). Based on construction of MRF energy function, spectral and directional features are extracted for on-board urban point clouds. The MRF energy function is consisted of unary and pairwise potentials. The unary terms are computed by SVM classifictaion. The initial labeling is mainly processed through geometrical shapes. The pairwise potential is estimated by Naïve Bayes. From training data, the probability of adjacent objects is computed by prior knowledge. The final labeling method is reweighted message-passing to minimiza

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Shu, Zhen, Kai Sun, Kaijin Qiu, and Kou Ding. "PAIRWISE-SVM FOR ON-BOARD URBAN ROAD LIDAR CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 109–13. http://dx.doi.org/10.5194/isprs-archives-xli-b1-109-2016.

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The common method of LiDAR classifications is Markov random fields (MRF). Based on construction of MRF energy function, spectral and directional features are extracted for on-board urban point clouds. The MRF energy function is consisted of unary and pairwise potentials. The unary terms are computed by SVM classifictaion. The initial labeling is mainly processed through geometrical shapes. The pairwise potential is estimated by Naïve Bayes. From training data, the probability of adjacent objects is computed by prior knowledge. The final labeling method is reweighted message-passing to minimiza

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Platias, C., M. Vakalopoulou, and K. Karantzalos. "AUTOMATIC MRF-BASED REGISTRATION OF HIGH RESOLUTION SATELLITE VIDEO DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 121–28. http://dx.doi.org/10.5194/isprsannals-iii-1-121-2016.

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In this paper we propose a deformable registration framework for high resolution satellite video data able to automatically and accurately co-register satellite video frames and/or register them to a reference map/image. The proposed approach performs non-rigid registration, formulates a Markov Random Fields (MRF) model, while efficient linear programming is employed for reaching the lowest potential of the cost function. The developed approach has been applied and validated on satellite video sequences from Skybox Imaging and compared with a rigid, descriptor-based registration method. Regard

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Platias, C., M. Vakalopoulou, and K. Karantzalos. "AUTOMATIC MRF-BASED REGISTRATION OF HIGH RESOLUTION SATELLITE VIDEO DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 121–28. http://dx.doi.org/10.5194/isprs-annals-iii-1-121-2016.

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In this paper we propose a deformable registration framework for high resolution satellite video data able to automatically and accurately co-register satellite video frames and/or register them to a reference map/image. The proposed approach performs non-rigid registration, formulates a Markov Random Fields (MRF) model, while efficient linear programming is employed for reaching the lowest potential of the cost function. The developed approach has been applied and validated on satellite video sequences from Skybox Imaging and compared with a rigid, descriptor-based registration method. Regard

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Pratomo, Jati, and Triyoga Widiastomo. "IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA." Geoplanning: Journal of Geomatics and Planning 3, no. 2 (2016): 127. http://dx.doi.org/10.14710/geoplanning.3.2.127-136.

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The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urb

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Yin, Junjun, Xiyun Liu, Jian Yang, Chih-Yuan Chu, and Yang-Lang Chang. "PolSAR Image Classification Based on Statistical Distribution and MRF." Remote Sensing 12, no. 6 (2020): 1027. http://dx.doi.org/10.3390/rs12061027.

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Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real Po

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Lee, Sangkyun, Piotr Sobczyk, and Malgorzata Bogdan. "Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control." Symmetry 11, no. 10 (2019): 1311. http://dx.doi.org/10.3390/sym11101311.

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In this paper, we propose a new estimation procedure for discovering the structure of Gaussian Markov random fields (MRFs) with false discovery rate (FDR) control, making use of the sorted ℓ 1 -norm (SL1) regularization. A Gaussian MRF is an acyclic graph representing a multivariate Gaussian distribution, where nodes are random variables and edges represent the conditional dependence between the connected nodes. Since it is possible to learn the edge structure of Gaussian MRFs directly from data, Gaussian MRFs provide an excellent way to understand complex data by revealing the dependence stru

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Huang, Tianle. "Research on the Integration and Application of CNN and MRF in Geotechnics." Highlights in Science, Engineering and Technology 106 (July 16, 2024): 267–78. http://dx.doi.org/10.54097/rk7xw167.

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Stratigraphic uncertainty can result in serious construction accidents or extra construction costs when the geotechnical information is limited. The Markov random field (MRF), based on the stochastic theory, provides engineers with a useful but time-consuming solution to predict the unknown stratum and relevant parameters under some priori knowledge. A new integrated model of convolutional neural network (CNN) and MRF has been proved to greatly improve the computation efficiency compared with the mentioned conventional method while keeping a relatively good accuracy, but it has not been widely

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Lenin Kumar Reddy, Sama, C. V. Rao, and P. Rajesh Kumar. "Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images." Russian Journal of Earth Sciences 21, no. 3 (2021): 1–9. http://dx.doi.org/10.2205/2021es000772.

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This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index, Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors

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Andrejchenko, Vera, Wenzhi Liao, Wilfried Philips, and Paul Scheunders. "Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields." Remote Sensing 11, no. 6 (2019): 624. http://dx.doi.org/10.3390/rs11060624.

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Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple featur

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Kumar Reddy, Sama Lenin, C. V. Rao, P. Rajesh Kumar, R. V. G. Anjaneyulu, and B. Gopala Krishna. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319. http://dx.doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Werbos, Paul J. "Stochastic Path Model of Polaroid Polarizer for Bell's Theorem and Triphoton Experiments." International Journal of Bifurcation and Chaos 25, no. 03 (2015): 1550046. http://dx.doi.org/10.1142/s0218127415500467.

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Depending on the outcome of the triphoton experiment now underway, it is possible that the new local realistic Markov Random Field (MRF) models will be the only models now available to correctly predict both that experiment and Bell's theorem experiments. The MRF models represent the experiments as graphs of discrete events over space-time. This paper extends the MRF approach to continuous time, by defining a new class of realistic model, the stochastic path model, and showing how it can be applied to ideal polaroid type polarizers in such experiments. The final section discusses possibilities

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Sama, Lenin Kumar Reddy, V. Rao C., Rajesh Kumar P., V. G. Anjaneyulu R., and Gopala Krishna B. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319–36. https://doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Wu, Yongji, Defu Lian, Yiheng Xu, Le Wu, and Enhong Chen. "Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1054–61. http://dx.doi.org/10.1609/aaai.v34i01.5455.

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The recent growth of social networking platforms also led to the emergence of social spammers, who overwhelm legitimate users with unwanted content. The existing social spammer detection methods can be characterized into two categories: features based ones and propagation-based ones. Features based methods mainly rely on matrix factorization using tweet text features, and regularization using social graphs is incorporated. However, these methods are fully supervised and can only utilize labeled part of social graphs, which fail to work in a real-world semi-supervised setting. The propagation-b

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Mei, Qipeng, Kevin Qiu, Dimitri Bulatov, and Dorota Iwaszczuk. "Improving Deep Learning based Point Cloud Classification using Markov Random Fields with Quadratic Pseudo-Boolean Optimization." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W5-2024 (June 27, 2024): 229–36. http://dx.doi.org/10.5194/isprs-annals-x-4-w5-2024-229-2024.

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Abstract. 3D point clouds are a relevant source of information for multiple applications, including digital twins, building modeling, disaster and risk management, forestry, autonomous driving, and many others. Assigning points to the semantic classes is one of the essential data interpretation steps to effectively use them for further analysis. Deep learning models for semantic segmentation, such as RandLA-Net, are state-of-the-art methods for this task. Although the overall accuracy of classification is usually satisfactory,there are still several shortcomings not allowing assigning correct

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He, Xu, and Yong Yin. "Non-Local and Multi-Scale Mechanisms for Image Inpainting." Sensors 21, no. 9 (2021): 3281. http://dx.doi.org/10.3390/s21093281.

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Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of

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Brimkulov, Ulan. "Matrices whose inverses are tridiagonal, band or block-tridiagonal and their relationship with the covariance matrices of a random Markov process." Filomat 33, no. 5 (2019): 1335–52. http://dx.doi.org/10.2298/fil1905335b.

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The article discusses the matrices of the form A1n, Amn, AmN, whose inverses are: tridiagonal matrix A-1n (n - dimension of the A-mn matrix), banded matrix A-mn (m is the half-width band of the matrix) or block-tridiagonal matrix A-m N (N = n x m - full dimension of the block matrix; m - the dimension of the blocks) and their relationships with the covariance matrices of measurements with ordinary (simple) Markov Random Processes (MRP), multiconnected MRP and vector MRP, respectively. Such covariance matrices frequently occur in the problems of optimal filtering, extrapolation and interpolatio

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Wang, Jie, Bensheng Huang, and Fuming Wang. "Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning." Water 15, no. 7 (2023): 1288. http://dx.doi.org/10.3390/w15071288.

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Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts. Synthetic aperture radar (SAR) is an all-weather, 24 h data source used to extract information about flood inundations, and its primary aim is to extract water body information for flood monitoring. In this study, we have studied the backscattering characteristics of water and non-water, combined the threshold segmentation method with Markov random

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Nex, F., E. Rupnik, I. Toschi, and F. Remondino. "Automated processing of high resolution airborne images for earthquake damage assessment." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 315–21. http://dx.doi.org/10.5194/isprsarchives-xl-1-315-2014.

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Emergency response ought to be rapid, reliable and efficient in terms of bringing the necessary help to sites where it is actually needed. Although the remote sensing techniques require minimum fieldwork and allow for continuous coverage, the established approaches rely on a vast manual work and visual assessment thus are time-consuming and imprecise. Automated processes with little possible interaction are in demand. This paper attempts to address the aforementioned issues by employing an unsupervised classification approach to identify building areas affected by an earthquake event. The clas

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Dong, Tianzhen, Yi Zhang, Mengying Li, and Yuntao Bai. "Point Cloud Repair Method via Convex Set Theory." Applied Sciences 13, no. 3 (2023): 1830. http://dx.doi.org/10.3390/app13031830.

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The point cloud is the basis for 3D object surface reconstruction. An incomplete point cloud significantly reduces the accuracy of downstream work such as 3D object reconstruction and recognition. Therefore, point-cloud repair is indispensable work. However, the original shape of the point cloud is difficult to restore due to the uncertainty of the position of the new filling point. Considering the advantages of the convex set in dealing with uncertainty problems, we propose a point-cloud repair method via a convex set that transforms a point-cloud repair problem into a construction problem of

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Cooper, M. C., and S. Zivny. "Tractable Triangles and Cross-Free Convexity in Discrete Optimisation." Journal of Artificial Intelligence Research 44 (July 27, 2012): 455–90. http://dx.doi.org/10.1613/jair.3598.

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The minimisation problem of a sum of unary and pairwise functions of discrete variables is a general NP-hard problem with wide applications such as computing MAP configurations in Markov Random Fields (MRF), minimising Gibbs energy, or solving binary Valued Constraint Satisfaction Problems (VCSPs). We study the computational complexity of classes of discrete optimisation problems given by allowing only certain types of costs in every triangle of variable-value assignments to three distinct variables. We show that for several computational problems, the only non- trivial tractable classes are t

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MELGANI, FARID. "CLASSIFICATION OF MULTITEMPORAL REMOTE-SENSING IMAGES BY A FUZZY FUSION OF SPECTRAL AND SPATIO-TEMPORAL CONTEXTUAL INFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 02 (2004): 143–56. http://dx.doi.org/10.1142/s0218001404003083.

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A fuzzy-logic approach to the classification of multitemporal, multisensor remote-sensing images is proposed. The approach is based on a fuzzy fusion of three basic sources of information: spectral, spatial and temporal contextual information sources. It aims at improving the accuracy over that of single-time noncontextual classification. Single-time class posterior probabilities, which are used to represent spectral information, are estimated by Multilayer Perceptron neural networks trained for each single-time image, thus making the approach applicable to multisensor data. Both the spatial a

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Abuhussein, Mohammed, and Aaron Robinson. "Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures." Journal of Imaging 8, no. 10 (2022): 266. http://dx.doi.org/10.3390/jimaging8100266.

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The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs to the network or as labels for training. The majority of the available image segmen

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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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Chen, S. Y., Hanyang Tong, and Carlo Cattani. "Markov Models for Image Labeling." Mathematical Problems in Engineering 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/814356.

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Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.

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Mat Said, K. A., and A. B. Jambek. "DNA Microarray Image Segmentation Using Markov Random Field Algorithm." Journal of Physics: Conference Series 2071, no. 1 (2021): 012032. http://dx.doi.org/10.1088/1742-6596/2071/1/012032.

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Abstract A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov rand

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Zhao, J., G. Huang, and Z. Zhao. "SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2371–74. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2371-2018.

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Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. 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. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundar

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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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Salih, Omran, and Serestina Viriri. "Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field." Symmetry 12, no. 8 (2020): 1224. http://dx.doi.org/10.3390/sym12081224.

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Markov random field (MRF) theory has achieved great success in image segmentation. Researchers have developed various methods based on MRF theory to solve skin lesions segmentation problems such as pixel-based MRF model, stochastic region-merging approach, symmetric MRF model, etc. In this paper, the proposed method seeks to provide a complement to the advantages of the pixel-based MRF model and stochastic region-merging approach. This is in order to overcome shortcomings of the pixel-based MRF model, because of various challenges that affect the skin lesion segmentation results such as irregu

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Chávez, Ricardo Omar, Hugo Jair Escalante, Manuel Montes-y-Gómez, and Luis Enrique Sucar. "Multimodal Markov Random Field for Image Reranking Based on Relevance Feedback." ISRN Machine Vision 2013 (February 11, 2013): 1–16. http://dx.doi.org/10.1155/2013/428746.

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This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider the problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to a query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the images in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked images. The MRF takes into account (1) the rank information provided by the initial

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Chyan, Phie, and N. Tri Saptadi. "Pemulihan Citra Berbasis Metode Markov Random Field." JURIKOM (Jurnal Riset Komputer) 9, no. 2 (2022): 218. http://dx.doi.org/10.30865/jurikom.v9i2.3966.

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Image processing and computer vision today are faced with increasing big data applications. Excessive collection of Image data sometimes can have bad quality due to errors at the time of acquisition or at the time of transmission, so for that problem the method is needed to perform image restoration. Image restoration is a process to make improvements to the image with the aim of obtaining a clean image from noise like the original image. Among the methods that can be used in image restoration, Markov Random Field (MRF) based on a probabilistic representation of image processing problems, name

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Jung, Myung Hee, Eui Jung Yun, and Sy Woo Byun. "Utilization of Markov Random Field for Large Images: Multiframe Work and Bayesian Approach." Key Engineering Materials 277-279 (January 2005): 183–88. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.183.

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Markov Random Field (MRF) models have been successfully utilized in many digital image processing problems such as texture modeling and region labeling. Although MRF provides a well-defined statistical approach for the analysis of images, one disadvantage is the expensive computational cost for the processing and sampling of large images, since global features are assumed to be specified through local descriptions. In this study, a methodology is explored that reduces the computational burden and increases the speed of image analysis for large images, especially airborne and space-based remote

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Jing, Junfeng, Qi Li, Pengfei Li, Hongwei Zhang, and Lei Zhang. "Image Segmentation of Printed Fabrics with Hierarchical Improved Markov Random Field in the Wavelet Domain." Journal of Engineered Fibers and Fabrics 11, no. 3 (2016): 155892501601100. http://dx.doi.org/10.1177/155892501601100305.

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An improved MRF algorithm–hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale causal MRF model and the intra-scale non-causal MRF model. After that, parameter estimation is the essential section in the inter-scale, enhancing modeling capabilities of the pixels partial dependency. S

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Panić, Marko, Dušan Jakovetić, Dejan Vukobratović, Vladimir Crnojević, and Aleksandra Pižurica. "MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior." Sensors 20, no. 11 (2020): 3185. http://dx.doi.org/10.3390/s20113185.

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Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a

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Rota, Gian-Carlo. "Markov random fields." Advances in Mathematics 57, no. 2 (1985): 208. http://dx.doi.org/10.1016/0001-8708(85)90060-x.

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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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