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Samuel, Kegan. "Gradient based MRF learning for image restoration and segmentation". Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5480.
Pełny tekst źródłaPh.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
Kato, Jien, Toyohide Watanabe, Sébastien Joga, Liu Ying, Hiroyuki Hase, ジェーン 加藤 i 豊英 渡邉. "An HMM/MRF-based stochastic framework for robust vehicle tracking". IEEE, 2004. http://hdl.handle.net/2237/6743.
Pełny tekst źródłaKarci, 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.
Pełny tekst źródłaGasnier, 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.
Pełny tekst źródłaSpaceborne 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
Besbes, Ahmed. "Image segmentation using MRFs and statistical shape modeling". Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00594246.
Pełny tekst źródłaKale, 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.
Pełny tekst źródłaWang, 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.
Pełny tekst źródłaStien, 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.
Pełny tekst źródłaWe 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.
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.
Pełny tekst źródłaDrouin, 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.
Pełny tekst źródłaCe 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)
Kirkland, Mark. "Simulation methods for Markov random fields". Thesis, University of Strathclyde, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278512.
Pełny tekst źródłaArnesen, Petter. "Approximate recursive calculations of discrete Markov random fields". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10806.
Pełny tekst źródłaIn this thesis we present an approximate recursive algorithm for calculations of discrete Markov random fields defined on graphs. We write the probability distribution of a Markov random field as a function of interaction parameters, a representation well suited for approximations. The algorithm we establish is a forward-backward algorithm, where the forward part recursively decomposes the probability distribution into a product of conditional distributions. Next we establish two different backward parts to our algorithm. In the first one we are able to simulate from the probability distribution, using the decomposed system. The second one enables us to calculate the marginal distributions for all the nodes in the Markov random field. All the approximations in our algorithm are controlled by a positive parameter, and when this parameter is equal to 0, our algorithm is by definition an exact algorithm. We investigate the performance of our algorithm by the CPU time, and by evaluating the quality of the approximations in various ways. As an example of the usage of our algorithm, we estimate an unknown picture from a degenerated version, using the marginal posterior mode estimate. This is a classical Bayesian problem.
Dror, Mizrahi Yariv. "Linear and parallel learning of Markov random fields". Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51458.
Pełny tekst źródłaScience, Faculty of
Mathematics, Department of
Graduate
Chandgotia, Nishant. "Markov random fields, Gibbs states and entropy minimality". Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52913.
Pełny tekst źródłaScience, Faculty of
Mathematics, Department of
Graduate
Sánchez-Ordoñez, Andrés E. "A Markov random fields approach to modelling habitat". Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54553.
Pełny tekst źródłaScience, Faculty of
Statistics, Department of
Graduate
Li, Chang-Tsun. "Unsupervised texture segmentation using multiresolution Markov random fields". Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/39307/.
Pełny tekst źródłaLienart, Thibaut. "Inference on Markov random fields : methods and applications". Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628.
Pełny tekst źródłaOlsen, Jessica Lyn. "An Applied Investigation of Gaussian Markov Random Fields". BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3273.
Pełny tekst źródłaIslam, Mofakharul University of Ballarat. "Unsupervised Color Image Segmentation Using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12827.
Pełny tekst źródłaMaster of Computing
Frondana, Iara Moreira. "Model selection for discrete Markov random fields on graphs". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-02022018-151123/.
Pełny tekst źródłaNesta tese propomos um critério de máxima verossimilhança penalizada para estimar o grafo de dependência condicional de um campo aleatório Markoviano discreto. Provamos a convergência quase certa do estimador do grafo no caso de um conjunto finito ou infinito enumerável de variáveis. Nosso método requer condições mínimas na distribuição de probabilidade e contrariamente a outras abordagens da literatura, a condição usual de positividade não é necessária. Introduzimos alguns exemplos com um conjunto finito de vértices e estudamos o desempenho do estimador em dados simulados desses exemplos. Também propomos um procedimento empírico baseado no método de validação cruzada para selecionar o melhor valor da constante na definição do estimador, e mostramos a aplicação deste procedimento em dois conjuntos de dados reais.
Dharmagunawardhana, Chathurika. "Image texture analysis based on Gaussian Markov Random Fields". Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/372489/.
Pełny tekst źródłaIslam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". Thesis, University of Ballarat, 2008. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/53709.
Pełny tekst źródłaMaster of Computing
Islam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15694.
Pełny tekst źródłaMaster of Computing
Chandgotia, Nishant. "Markov random fields and measures with nearest neighbour Gibbs potential". Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37000.
Pełny tekst źródłaMilun, Davin. "Generating Markov random field image analysis systems from examples". Buffalo, N.Y. : Dept. of Computer Science, State University of New York at Buffalo, 1995. http://www.cse.buffalo.edu/tech%2Dreports/95%2D23.ps.Z.
Pełny tekst źródłaCaputo, Barbara. "A new kernel method for object recognition:spin glass-Markov random fields". Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-58.
Pełny tekst źródłaRecognizing objects through vision is an important part of our lives: we recognize people when we talk to them, we recognize our cup on the breakfast table, our car in a parking lot, and so on. While this task is performed with great accuracy and apparently little effort by humans, it is still unclear how this performance is achieved. Creating computer methods for automatic object recognition gives rise to challenging theoretical problems such as how to model the visual appearance of the objects or categories we want to recognize, so that the resulting algorithm will perform robustly in realistic scenarios; to this end, how to use effectively multiple cues (such as shape, color, textural properties and many others), so that the algorithm uses uses the best subset of cues in the most effective manner; how to use specific features and/or specific strategies for different classes.
The present work is devoted to the above issues. We propose to model the visual appearance of objects and visual categories via probability density functions. The model is developed on the basis of concepts and results obtained in three different research areas: computer vision, machine learning and statistical physics of spin glasses. It consists of a fully connected Markov random field with energy function derived from results of statistical physics of spin glasses. Markov random fields and spin glass energy functions are combined together via nonlinear kernel functions; we call the model Spin Glass-Markov Random Fields. Full connectivity enables to take into account the global appearance of the object, and its specific local characteristics at the same time, resulting in robustness to noise, occlusions and cluttered background. Because of properties of some classes of spin glasslike energy functions, our model allows to use easily and effectively multiple cues, and to employ class specific strategies. We show with theoretical analysis and experiments that this new model is competitive with state-of-the-art algorithms for object recognition.
Altaye, Endale Berhane. "Approximate recursive algorithm for finding MAP of binary Markov random fields". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10824.
Pełny tekst źródłaThe purpose of this study was to develop a recursive algorithm for computing a maximum a posteriori (MAP) estimate of a binary Markov random field (MRF) by using the MAP-MRF framework. We also discuss how to include an approximation in the recursive scheme, so that the algorithm becomes computationally feasible also for larger problems. In particular, we discuss how our algorithm can be used in an image analysis setting. We consider a situation where an unobserved latent field is assumed to follow a Markov random field prior model, a Gaussian noise-corrupted version of the latent field is observed, and we estimate the unobserved field by the MAP estimator.
Siksik, Ola. "Markov random fields in visual reconstruction : a transputer-based multicomputer implementation". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28863.
Pełny tekst źródłaScience, Faculty of
Computer Science, Department of
Graduate
Fernandez-Duran, Juan Jose. "Statistical techniques for clutter removal and attentuation detection in weather radar data". Thesis, University of Essex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243358.
Pełny tekst źródłaToftaker, Håkon. "Automatic Parametrisation and Block pseudo Likelihood Estimation for binary Markov random Fields". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9705.
Pełny tekst źródłaDiscrete Markov random fields play an important role in spatial statistics, and are applied in many different areas. Models which consider only pairwise interaction between sites such as the Ising model often perform well as a prior in a Bayesian setting but are generally unable to provide a realistic representation of a typical scene. Models which are defined by considering more than only two points have been shown to do well in describing many different types of textures. The specification of such models is often rather tedious, both in defining the parametric model, and in estimating the parameters. In this paper we present a procedure which in an automatic fashion defines a parametric model from a training image. On the basis of the frequencies of the different types of local configurations we define the potential function of all the different clique configurations from a relatively small number of parameters. Then we make use of a forward-backward algorithm to compute a maximum block pseudo likelihood estimator for the parametric models resulting from the automatic procedure. Then this set of methods is used to define Markov random field models from three different training images. The same algorithm which is used to calculate the block pseudo likelihood is used to implement a block Gibbs sampler. This is used to explore the properties of the models through simulation. The procedure is tested for a set of different input values. The analysis shows that the procedure is quite able to produce a reasonable presentation for one of the training images but performs insufficiently on the others. The main problem seems to be the ratio between black and white, and this seems to be a problem caused mainly by the estimator. It is therefore difficult to make a conclusion about the quality of the parametric model. We also show that by modifying the estimated potential function slightly we can get a model which is able to describe the second training image quite well.
Altay, Suhan. "On Forward Interest Rate Models: Via Random Fields And Markov Jump Processes". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608342/index.pdf.
Pełny tekst źródłaCaputo, Barbara. "A new kernel method for object recognition : spin glass-Markov random fields /". Stockholm, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-58.
Pełny tekst źródłaMuffert, Maximilian [Verfasser]. "Incremental Map Building with Markov Random Fields and its Evaluation / Maximilian Muffert". Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1155302990/34.
Pełny tekst źródłaDai, Zhenwen, i 戴振文. "A Markov random field approach for multi-view normal integration". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4308588X.
Pełny tekst źródłaDai, Zhenwen. "A Markov random field approach for multi-view normal integration". Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4308588X.
Pełny tekst źródłaWehmann, Adam. "A Spatial-Temporal Contextual Kernel Method for Generating High-Quality Land-Cover Time Series". The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398866264.
Pełny tekst źródłaMinin, Volodymyr. "Exploring evolutionary heterogeneity with change-point models, Gaussian Markov random fields, and Markov chain induced counting processes". Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1320942721&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Pełny tekst źródłaFauske, Johannes. "An empirical study of the maximum pseudo-likelihood for discrete Markov random fields". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9949.
Pełny tekst źródłaIn this text we will look at two parameter estimation methods for Markov random fields on a lattice. They are maximum pseudo-likelihood estimation and maximum general pseudo-likelihood estimation, which we abbreviate MPLE and MGPLE. The idea behind them is that by maximizing an approximation of the likelihood function, we avoid computing cumbersome normalising constants. In MPLE we maximize the product of the conditional distributions for each variable given all the other variables. In MGPLE we use a compromise between pseudo-likelihood and the likelihood function as the approximation. We evaluate and compare the performance of MPLE and MGPLE on three different spatial models, which we have generated observations of. We are specially interested to see what happens with the quality of the estimates when the number of observations increases. The models we use are the Ising model, the extended Ising model and the Sisim model. All the random variables in the models have two possible states, black or white. For the Ising and extended Ising model we have one and three parameters respectively. For Sisim we have $13$ parameters. The quality of both methods get better when the number of observations grow, and MGPLE gives better results than MPLE. However certain parameter combinations of the extended Ising model give worse results.
Villanueva-Morales, Antonio. "Modified pseudo-likelihood estimation for Markov random fields with Winsorized Poisson conditional distributions". [Ames, Iowa : Iowa State University], 2008.
Znajdź pełny tekst źródłaTang, Kam-Lun. "A Markov random field formulation for dense photometric stereo : theory, practice and applications /". View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20TANG.
Pełny tekst źródłaShakya, Siddhartha. "DEUM : a framework for an estimation of distribution algorithm based on Markov random fields". Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/39.
Pełny tekst źródłaWang, Shiyun. "Connection between Graphical Potential Games and Markov Random Fields with an Extension to Bayesian Networks". Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10785804.
Pełny tekst źródłaA probabilistic graphical model is a graphical representation of a joint probability distribution, in which the conditional independencies among random variables are specified via an underlying graph. We connect the probabilistic graphical models to some special types of games. Graphical potential games are the intersection of potential games and graphical games. They have characteristics from both of these two classes of games, namely, potential functions of potential games and graphical structure of graphical games. We review that there is a bijection between the normalized graphical potential games and the corresponding Markov Random Fields. We use a similar method to study the structure of Bayesian networks and define two types of games on directed graphs whose nodes are players. One is the directed graphical game, which is defined based on the assumption that the utility of player i only depends on the parent of i in the graph. The other one is the Bayesian-factorable potential game. The potential function of the game gives rise to the probability distribution, which can be factorized as in a Bayesian network. We explore the connections between such games and Bayesian networks.
Marqués, Acosta Fernando. "Multiresolution image segmentation based on camporend random fields: Application to image coding". Doctoral thesis, Universitat Politècnica de Catalunya, 1992. http://hdl.handle.net/10803/6910.
Pełny tekst źródłaEn esta tesis se introduce un nuevo tipo de modelo denominado modelo jerárquico, ya que tiene dos niveles diferentes sobrepuestos uno sobre el otro. El nivel inferior (o subyacente) modela la posición que ocupa cada una de las regiones dentro de la imagen; mientras que, por su parte, el nivel superior (u observable) esta compuesto por un conjunto de submodelos independientes (un submodelo por región) que caracterizan el comportamiento del interior de las regiones. Para el primero se usa un campo aleatorio Markoviano de orden dos que modelara los contornos de las regiones, mientras que para el segundo nivel se usa un modelo Gausiano.
En el trabajo se estudian los mejores potenciales que deben asignarse a los tipos de agrupaciones que permiten definir los contornos. Con todo ello la segmentación se realiza buscando la partición más probable (criterio MAP) para una realización concreta (imagen observable).
El proceso de búsqueda de la partición optima para imágenes del tamaño habitual seria prácticamente inviable desde un punto de vista de tiempo de cálculo. Para que se pueda realizar debe partirse de una estimación inicial suficientemente buena y de una algoritmo rápido de mejora como es una búsqueda local. Para ello se introduce la técnica de segmentación piramidal (multirresolucion). La pirámide se genera con filtrado Gausiano y diezmado. En el nivel mas alto de la pirámide, al tener pocos píxels, si que se puede encontrar la partición óptima.
Kornak, John. "Bayesian spatial inference from haemodynamic response parameters in functional magnetic resonance imaging". Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325718.
Pełny tekst źródłaKim, Kyu-Heon. "Segmentation of natural texture images using a robust stochastic image model". Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307927.
Pełny tekst źródłaZhang, Xiaodan Hu Xiaohua. "Exploiting external/domain knowledge to enhance traditional text mining using graph-based methods /". Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3076.
Pełny tekst źródłaLiang, Dong. "Issues in Bayesian Gaussian Markov random field models with application to intersensor calibration". Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/400.
Pełny tekst źródłaZhu, Ying. "Signal detection on two-dimensional intersymbol interference channels correlated sources and reduced complexity algorithms /". [Pullman, Wash.] : Washington State University, 2008. http://www.dissertations.wsu.edu/Dissertations/Fall2008/y_zhu_081408.pdf.
Pełny tekst źródłaTitle from PDF title page (viewed on Sept. 23, 2008) "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 83-90).
Tran, The Truyen. "On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling". Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/436.
Pełny tekst źródłaKutarnia, Jason Francis. "A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation". Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/369.
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