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

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Published: 1 June 2024

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

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

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 (November 1, 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. Third, based on the Vietnamese and Chinese database we analyze the efficiency of our emotion recognition system. We adopt the dimensional emotion model (arousal-valence) to verify the efficiency of MRF configuration method. The experimental results show that the high-order Markov random fields can improve the dimensional emotion recognition in the cross-language experiments, and the configuration method shows promising robustness over different languages.
2

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 (July 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, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.
3

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 (February 1, 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 extensions to account for nonstationarity, discontinuity, and varying spatial properties at various scales of resolution are easily accessible in the MRF framework. Our proposed method is computationally efficient and well suited to reconstruct fine-scale spatial fields from coarser, multiscale samples (based on seismic and production data) and sparse fine-scale conditioning data (e.g., well data). It is easy to implement, and it can account for the complex, nonlinear interactions between different scales, as well as the precision of the data at various scales, in a consistent fashion. We illustrate our method with a variety of examples that demonstrate the power and versatility of the proposed approach. Finally, a comparison with Sequential Gaussian Simulation with Block Kriging (SGSBK) indicates similar performance with less restrictive assumptions. Introduction A persistent problem in petroleum reservoir characterization is to build a model for flow simulations based on incomplete information. Because of the limited spatial information, any conceptual reservoir model used to describe heterogeneities will, necessarily, have large uncertainty. Such uncertainties can be significantly reduced by integrating multiple data sources into the reservoir model.1 In general, we have hard data, such as well logs and cores, and soft data, such as seismic traces, production history, conceptual depositional models, and regional geological analyses. Integrating information from this wide variety of sources into the reservoir model is not a trivial task. This is because different data sources scan different length scales of heterogeneity and can have different degrees of precision.2 Reconciling multiscale data for spatial modeling of reservoir properties is important because different data types provide different information about the reservoir architecture and heterogeneity. It is essential that reservoir models preserve small-scale property variations observed in well logs and core measurements and capture the large-scale structure and continuity observed in global measures such as seismic and production data. A hierarchical model is particularly well suited to address the multiscaled nature of spatial fields, match available data at various levels of resolution, and account for uncertainties inherent in the information.1–3 Several methods to combine multiscale data have been introduced in the literature, with a primary focus on integrating seismic and well data.3–9 These include conventional techniques such as cokriging and its variations,3–6 SGSBK,7 and Bayesian updating of point kriging.8,9 Most kriging-based methods are restricted to multi-Gaussian and stationary random fields.3–9 Therefore, they require data transformation and variogram construction. In practice, variogram modeling with a limited data set can be difficult and strongly user-dependent. Improper variograms can lead to errors and inaccuracies in the estimation. Thus, one might also need to consider the uncertainty in variogram models during estimation. 10 However, conventional geostatistical methods do not provide an effective framework to account for the uncertainty of the variogram. Furthermore, most of the multiscale integration algorithms assume a linear relationship between the scales. The objective of this paper is to introduce a novel multiscale data-integration technique that provides a flexible and sound mathematical framework to overcome some of the limitations of conventional geostatistical techniques. Our approach is based on multiscale MRFs11–14 that can effectively integrate multiple data sources into high-resolution reservoir models for reliable reservoir forecasting. This proposed approach is also ideally suited to simulation- based computations, such as MCMC.15,16 Methodology Our problem of interest is to generate fine-scale random fields based on sparse fine-scale samples and coarse-scale data. Such situations arise when we have limited point measurements, such as well data, and coarse-scale information based on seismic and/or production data. Our proposed method is a Bayesian approach to spatial modeling based on MRF and multiresolution algorithms in image analysis. Broadly, the method consists of two major parts:construction of a posterior distribution for multiscale data integration using a hierarchical model andimplementing MCMC to explore the posterior distribution. Construction of a Posterior Distribution for Multiscale Data Integration. A multiresolution MRF provides an efficient framework to integrate different scales of data hierarchically, provided that the coarse-scale resolution is dependent on the next finescale resolution.11 In general, a hierarchical conditional model over scales 1,. . ., N (from fine to coarse) can be expressed in terms of the product of conditional distributions,Equation 1 where p(xn), n=1, . . ., N, are MRF models at each scale, and the terms p(xn|xn-1) express the statistical interactions between different scales. This approach links the various scales stochastically in a direct Bayesian hierarchical modeling framework (Fig. 1). Knowing the fine-scale field xn does not completely determine the field at a coarser scale xn+1, but depending on the extent of the dependence structure modeled and estimated, it influences the distribution at the coarser scales to a greater or lesser extent. This enables us to address multiscale problems accounting for the scale and precision of the data at various levels. For clarity of exposition, a hierarchical model for reconciling two different scales of data will be considered below.Equation 2 From this equation, the posterior distribution of the fine-scale random field indexed by 1 given a coarse-scale random field indexed by 2 can be derived as follows.
4

Yang, Xiangyu, Xuezhi Yang, Chunju Zhang, and Jun Wang. "SAR Image Classification Using Markov Random Fields with Deep Learning." Remote Sensing 15, no. 3 (January 20, 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 the distribution of spatial relationships between adjacent superpixels. Thirdly, a novel region-level MRF is employed to classify the superpixels, which combines the intensity field and probability field in one framework. In our algorithm, the generation of superpixels reduces the misclassification at the pixel level, and region-level misclassification is rectified by the improvement of spatial description. Experimental results on simulated and real SAR images confirm the efficacy of the proposed algorithm for classification.
5

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 and node semantic information in a complete end-to-end deep network architecture. Our extensive experiments demonstrate the superior performance of the new method over state-of-the-art methods and its scalability on several large benchmark problems.
6

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 image processing where the image intensity at a particular location depends only on a neighborhood of pixels.</p></abstract>
7

Kurella, Pushpak. "Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection." International Transactions on Electrical Engineering and Computer Science 2, no. 4 (December 30, 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 reduction of energy consumption and the smoothing of images. In this work, the primary goal is to increase segmentation quality by implementing a unique Hidden Markov Random field model and employing MATLAB simulations to implement in Spatial Fuzzy, Iterative Conditional Mode (ICM) method, Fuzzy MRF technique, and Hidden Markov Random field model. The results will be compared to those obtained using Histogram thresholding, the Region Growing method (RGM), the k-means methodology, and the Expectation and Maximization methods to assess segmentation quality and noise reduction.
8

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 (November 12, 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 information, which caused poor detectability. Therefore, this paper proposes a novel MRF-based method (ACEagle), considering node-level and community-level behavior features. Our method has several advantages: (1) based on the analysis of the nodes’ local structure, the community-level behavioral features are combined to calculate the nodes’ prior probability to close the ground truth, (2) it measured the behavior’s collaborative intensity between nodes by time and weight, constructing MRF by the synergic relationship exceeding the threshold to filter irrelevant structural information, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available. Through experiments in user-reviewed datasets where abnormal collusive behavior is most typical, the results show that ACEagle is significantly outperforming state-of-the-art baselines in collusive anomalies detection.
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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 huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.</p> </abstract>
10

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 (November 18, 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 function with probability [Formula: see text].
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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "MRF, Markov Random Fields":

1

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. While previous work has relied on time-consuming iterative approximations or stochastic approximations, this work will demonstrate how implicit differentiation can be used to analytically differentiate the overall training loss with respect to the MRF parameters. This framework leads to an efficient, flexible learning algorithm that can be applied to a number of different models. The effectiveness of the proposed learning method will then be demonstrated by learning the parameters of two related models applied to the task of denoising images. The experimental results will demonstrate that the proposed learning algorithm is comparable and, at times, better than previous training methods applied to the same tasks. A new segmentation model will also be introduced and trained using the proposed learning method. The proposed segmentation model is based on an energy minimization framework that is novel in how it incorporates priors on the size of the segments in a way that is straightforward to implement. While other methods, such as normalized cuts, tend to produce segmentations of similar sizes, this method is able to overcome that problem and produce more realistic segmentations.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
2

Kato, Jien, Toyohide Watanabe, Sébastien Joga, Liu Ying, Hiroyuki Hase, ジェーン 加藤, and 豊英 渡邉. "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 efficient algorithm to minimize levelable second and third order MRF energies, which is composed of a series of maximum flow/minimum cut computations, is proposed and applications of the proposed algorithm to image restoration are given.
4

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 car il est le premier satellite qui embarque un altimètre de fauchée quasi-nadir. Le calcul des hauteurs d’eau est fait par interférométrie sur les images SAR acquises par SWOT. La détection d’eau dans ces images est donc une étape essentielle du traitement des données SWOT, mais qui peut être difficile, en particulier avec un faible rapport signal sur bruit ou en présence de radiométries inhabituelles. Dans cette thèse, nous cherchons à développer de nouvelles méthodes pour rendre la détection d’eau plus robustes. Pour cela, nous nous intéressons à l’utilisation de données exogènes pour guider la détection, à la combinaison de données multi-temporelles et multi-capteurs et à des approches de débruitage. La première méthode proposée exploite les informations de la base de donnée des rivières utilisée par SWOT pour détecter les rivières fines dans l’image de façon robuste à la fois aux bruit dans l’image, aux erreurs éventuelles de la base de données et aux changements survenus. Cette méthode s’appuie sur un nouveau détecteur de structures linéiques, un algorithme de chemin de moindre coût et une nouvelle méthode de segmentation par CRF qui combine des termes d’attache aux données et de régularisation adaptés au problème. Nous avons également proposé une méthode dérivée des GrabCut qui utilise un polygone a priori contenant un lac pour le détecter sur une image SAR ou une série temporelle. Dans ce cadre, nous avons également étudié le recours à une combinaison multi-temporelle et multi-capteurs (optique et SAR). Enfin, dans le cadre d’une étude préliminaire sur les méthodes de débruitage pour la détection d’eau nous avons étudié les propriétés statistiques de la moyenne géométrique temporelle et proposé une adaptation de la méthode variationnelle MuLoG pour la débruiter
Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it
5

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'estimation des fonctions de densité de probabilité des longueurs de cordes normalisées. Dans une deuxième étape, nous construisons un algorithme de segmentation qui intègre le modèle statistique de forme, et qui le relie à l'image grâce à un terme région, à travers l'utilisation de diagrammes de Voronoi. Dans cette approche, un contour de forme déformable évolue vers l'objet à segmenter. Nous formulons aussi un algorithme de segmentation basé sur des détecteurs de points d'intérêt, où le terme de régularisation est lié à l'apriori de forme. Dans ce cas, on cherche à faire correspondre le modèle aux meilleurs points candidats extraits de l'image par le détecteur. L'optimisation pour les deux algorithmes est faite en utilisant des méthodes récentes et efficaces. Nous validons notre approche à travers plusieurs jeux de données en 2D et en 3D, pour des applications de vision par ordinateur ainsi que l'analyse d'images médicales.
6

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 Imaging (MRI) data, Computerized Tomography (CT) data and unified data, in which case, information coming from both modalities are utilized. The methods are applied both in three dimensions (3D) and two dimensions (2D) data cases. A simulation data and two patient data are utilized for tests. The patient data results are compared statistically with ground truth data which was labeled by an expert radiologist.
7

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 evaluation of the preterm brain. The segmentation part poses the major challenges of this task: insufficient spatial prior information of myelination and the presence of substantial partial volume voxels in clinical data. Specific spatial priors for the developing brain are obtained from either probabilistic atlases or manually annotated training images, but none of them currently include myelin as an individual tissue type. This causes further difficulties in partial volume estimation which depends on the probabilistic atlases of the composing pure tissues. Our key contribution is the development of an expectation-maximisation framework that incorporates an explicit partial volume class whose locations are configured in relation to the composing pure tissues in a predefined region of interest via second-order Markov random fields. This approach resolves the above challenges without requiring any probabilistic atlas of myelin. We also investigate atlas-based whole brain segmentation that generates the binary mask for the region of interest. We then construct a spatio-temporal growth model for myelin-like signals using logistic regression based on the automatic segmentations of 114 preterm infants aged between 29 and 44 gestational weeks. Lastly, we demonstrate the ability of age estimation using the normal growth model in a leave-one-out procedure.
8

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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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 second estimator, which is designed to match the model with the observation with respect to a set of specified functions. This estimator is only considered for the sMRF model, since that model proved to be flexible enough to give satisfying results. Due to time limitation of this thesis, we could not wait for the optimization of the estimator to converge. Thus, we can not evaluate this estimator. Finally, we extract useful information from the two-dimensional models and specify a sMRF model in three dimensions. Parameter estimation for this model needs approximative techniques, since we only have given observations in two dimensions. Such techniques have not been investigated in this report, however, we have adjusted the parameters manually and observed that the model is very flexible and might give very satisfying results.

9

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 of a binary image representing the associated user-specified segmentation. The segmentation for a new frame is generated by pasting in, for each image patch, the nearest neighbor from the training set. A mechanism inspired by belief propagation is used to insure consistency between neighboring patches. The algorithm is applied at different scale levels of the input images to take into account longer range interactions. A performance metric is defined for the automatic segmentation and the segmentation results are compared with a set of video sequences that have been entirely traced by hand. A new technique is also presented to automatically choose the optimal training data for the statistical model. A crude segmentation is computed from the smallest possible training set (one frame). A statistical analysis of this segmentation is then used to determine which other frames should be added to the training set in order to get the best possible segmentation. Finally, it is shown how the technique used for segmentation can be extended to perform example-based filtering of video and thus allow the creation of general-purpose rotoscoping systems.
Ce mémoire présente un modèle statistique ainsi que son implantation dans un programme de rotoscopie qui peut être utilisé pour la production de films d'animation. Le problème de la segmentation assistée de scènes video contenant un avant-plan et un arrière-plan distincts, un sous-ensemble du problème plus général que constitue la rotoscopie, est utilisé pour analyser les propriétés du modèle statistique et de son implantation. Le modèle statistique utilisé est construit à partir d'un découpage de paires d'images d'entraînement composées d'un cadre de la séquence video à segmenter et d'une image binaire qui défini la segmentation associée. La segmentation de chaque cadre de la sequence est obtenue en collant, pour chaque portion d'image, la portion d'image la plus similaire de l'ensemble d'entraînement. Un mécanisme inspiré de la "propagation de conviction"(belief propagation) est utilisé pour assurer la cohérence entre les portions de l'image de sortie qui sont voisines. L'algorithme est appliqué à plusieurs niveaux d'échelle afin de considérer la dépendance statistique de plus longue portée qui existe entre les pixels d'une image. Une métrique est définie pour mesurer la performance de la segmentation automatique. Les résultats de la segmentation sont analysés à l'aide d'une série de séquences vidéo qui ont préalablement été segmentées manuellement. Une nouvelle technique est également présentée pour permettre au logiciel de segmentation de choisir automatiquement l'ensemble d'entraînement optimal. Une segmentation grossière est d'abord obtenue en ulitisant le plus petit ensemble d'entraînement possible (1 cadre)
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Books on the topic "MRF, Markov Random Fields":

1

Kindermann, Ross. Markov random fields and their applications. [Providence]: AMS, 2003.

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

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Andrew, Blake. Markov random fields for vision and image processing. Cambridge, Mass: MIT Press, 2011.

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

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

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

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Gimelʹfarb, Georgiĭ Lʹvovich. Image textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.

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Hu, Dihe. Sui ji huan jing zhong de Ma'erkefu guo cheng =: Markov processes in random environments = Suiji huanjingzhong de Maerkefu guocheng. 8th ed. Beijing Shi: Gao deng jiao yu chu ban she, 2011.

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

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Evstigneev, I. V. Markov fields over countable partially ordered sets: Extrema and splitting. Providence, R.I: American Mathematical Society, 1994.

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

1

Shekhar, Shashi, and Hui Xiong. "Markov Random Field (MRF)." In Encyclopedia of GIS, 637. Boston, MA: 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, 131–56. Tokyo: 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, 599–614. Cham: 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, 37–61. Tokyo: 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, 101–30. Tokyo: 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, 233–40. Berlin, Heidelberg: 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, 83–99. London: 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, 205–9. Berlin, Heidelberg: 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, 179–214. New York, NY: 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, 189–226. Boston, MA: 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":

1

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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.403.

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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. Washington, D.C.: 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.
3

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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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}. California: 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 structures that appeal to human intuition and understanding, while simultaneously being able to learn structures from data, thus learning structures that have both the desirable qualities of interpretability and good prediction performance. The asynchronous nature of our algorithm further provides the ability to learn diverse structures via exploration, while remaining scalable. We demonstrate the ability of the models to learn semantically meaningful structures that also achieve better prediction performance when compared with a greedy search algorithm, a path-based algorithm, and manually defined clauses on two computational social science applications: i) modeling recovery in alcohol use disorder, and ii) detecting bullying.
6

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}. California: 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 correlations among the news articles (including labeled and unlabeled data). Specifically, we first leverage a DNN-based module to learn implicit relations, which we then reveal as the unary function of MRF. Pairwise functions with refining effects to encapsulate human insights are designed to capture the explicit association among all samples. Meanwhile, an event removal module is introduced to remove event impact on pairwise functions. Note that we train GDMRFF with the semi-supervised setting, which decreases the reliance on labeled data while maximizing the potential of unlabeled data. We further develop an Ambiguity Learning Guided MRF (ALGM) model as a concretization of GDMRFF. Experiments show that ALGM outperforms the compared methods significantly on two datasets, especially when labeled data is limited.
7

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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He, Dongxiao, Wenze Song, Di Jin, Zhiyong Feng, and Yuxiao Huang. "An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/794.

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Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network topology while ignore the semantic attributes. A straightforward way to combine the two types of information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then take this result as a prior to define the MRF model. In this way, however, the parameters of the two models cannot be adjusted by each other, preventing it from really realizing the complementation of the advantages of the two. This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly. However, LDA is a directed graphic model whereas MRF is undirected, making their integration a challenge. To handle this problem, we first transform LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage of the strength of both LDA and MRF. Empirical results show that our approach compares favorably with the state-of-the-art methods.
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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":

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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