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Dissertations / Theses on the topic 'MRF, Markov Random Fields'

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Published: 1 June 2024
Last updated: 31 July 2025

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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.
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2

Kato, Jien, Toyohide Watanabe, Sébastien Joga, et al. "An HMM/MRF-based stochastic framework for robust vehicle tracking." IEEE, 2004. http://hdl.handle.net/2237/6743.

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3

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

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A feature of minimizing images of a class of binary Markov random field energies is introduced and proved. Using this, the collection of minimizing images of levels of higher order, levelable MRF energies is shown to be a monotone collection. This implies that these images can be combined to give minimizing images of the MRF energy itself. Due to the recent developments, second and third order binary MRF energies of the mentioned class are known to be exactly minimized by maximum flow/minimum cut computations on appropriately constructed graphs. With the aid of these developments an exact and
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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 ca
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Besbes, Ahmed. "Image segmentation using MRFs and statistical shape modeling." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00594246.

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

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

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In this thesis, we aim to investigate the correlation between myelination and the gestational age for preterm infants, with the former being an important developmental process during human brain maturation. Quantification of myelin requires dedicated imaging, but the conventional magnetic resonance images routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. This thesis thus focuses on structural segmentation and spatio-temporal modelling of the so-called myelin-like signals on T2-weighted scans for early prognostic evaluati
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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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<p>We have been given a two-dimensional image of a geological structure. This structure is used to construct a three-dimensional statistical model, to be used as prior knowledge in the analysis of seismic data. We consider two classes of discrete lattice models for which efficient simulation is possible; sequential Markov random field (sMRF) and Markov mesh random field (MMRF). We first explore models from these two classes in two dimensions, using the maximum likelihood estimator (MLE). The results indicate that a larger neighbourhood should be considered for all the models. We also develop a
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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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10

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

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This thesis presents a statistical framework and its implementation in a user-assisted rotoscoping program intended for the production of animation movies. User-assisted video segmentation of scenes with well-defined foreground and background, a special case of the general problem of rotoscoping, is used to analyze the properties of the framework and its implementation. The statistical model used in the framework is built from pairs of training images composed of a frame from the sequence to segment and o
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11

Kirkland, Mark. "Simulation methods for Markov random fields." Thesis, University of Strathclyde, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278512.

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12

Arnesen, 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.

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<p>In 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 distrib
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13

Dror, Mizrahi Yariv. "Linear and parallel learning of Markov random fields." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51458.

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In this thesis, we introduce a new class of embarrassingly parallel parameter learning algorithms for Markov random fields (MRFs) with untied parameters, which are efficient for a large class of practical models. The algorithms parallelize naturally over cliques and, for graphs of bounded degree, have complexity that is linear in the number of cliques. We refer to these algorithms with the acronym LAP, which stands for Linear And Parallel. Unlike their competitors, the marginal versions of the proposed algorithms are fully parallel and for log-linear models they are also data efficient, requi
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Chandgotia, Nishant. "Markov random fields, Gibbs states and entropy minimality." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52913.

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The well-known Hammersley-Clifford Theorem states (under certain conditions) that any Markov random field is a Gibbs state for a nearest neighbour interaction. Following Petersen and Schmidt we utilise the formalism of cocycles for the homoclinic relation and introduce "Markov cocycles", reparametrisations of Markov specifications. We exploit this formalism to deduce the conclusion of the Hammersley-Clifford Theorem for a family of Markov random fields which are outside the theorem's purview (including Markov random fields whose support is the d-dimensional "3-colored chessboard"). On the othe
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Sánchez-Ordoñez, Andrés E. "A Markov random fields approach to modelling habitat." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54553.

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Habitat modelling presents a challenge due to the variety of data available and their corresponding accuracy. One option is to use Markov random fields as a way to incorporate these distinct types of data for habitat modelling. In this work, I provide a brief overview of the intuition, mathematical theory, and application considerations behind modelling habitat under this framework. In particular, an auto-logistic model is built and applied to modelling sea lion habitat using synthetic data. First, we explore modelling one sample of data. Afterwards, the framework is extended to the multi-samp
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16

Li, Chang-Tsun. "Unsupervised texture segmentation using multiresolution Markov random fields." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/39307/.

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In this thesis, a multiresolution Markov Random Field (MMRF) model for segmenting textured images without supervision is proposed. Stochastic relaxation labelling is adopted to assign the class label with highest probability to the block (site) being visited. Class information is propagated from low spatial resolution to high spatial resolution, via appropriate modifications to the interaction energies defining the field, to minimise class-position uncertainty. The thesis contains novel ideas presented in Chapter 4 and 5, respectively. In Chapter 4, the Multiresolution Fourier Transform (MFT)
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17

Lienart, 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.

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This thesis considers the problem of performing inference on undirected graphical models with continuous state spaces. These models represent conditional independence structures that can appear in the context of Bayesian Machine Learning. In the thesis, we focus on computational methods and applications. The aim of the thesis is to demonstrate that the factorisation structure corresponding to the conditional independence structure present in high-dimensional models can be exploited to decrease the computational complexity of inference algorithms. First, we consider the smoothing problem on Hid
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18

Olsen, Jessica Lyn. "An Applied Investigation of Gaussian Markov Random Fields." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3273.

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Recently, Bayesian methods have become the essence of modern statistics, specifically, the ability to incorporate hierarchical models. In particular, correlated data, such as the data found in spatial and temporal applications, have benefited greatly from the development and application of Bayesian statistics. One particular application of Bayesian modeling is Gaussian Markov Random Fields. These methods have proven to be very useful in providing a framework for correlated data. I will demonstrate the power of GMRFs by applying this method to two sets of data; a set of temporal data involving
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19

Islam, 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.

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We propose a novel approach to investigate and implement unsupervised segmentation of color images particularly natural color images. The aim is to devise a robust unsu- pervised segmentation approach that can segment a color textured image accurately. Here, the color and texture information of each individual pixel along with the pixel's spatial relationship within its neighborhood have been considered for producing precise segmentation of color images. Precise segmentation of images has tremendous potential in various application domains like bioinformatics, forensics, security and surveilla
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20

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/.

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In this thesis we propose to use a penalized maximum conditional likelihood criterion to estimate the graph of a general discrete Markov random field. We prove the almost sure convergence of the estimator of the graph in the case of a finite or countable infinite set of variables. Our method requires minimal assumptions on the probability distribution and contrary to other approaches in the literature, the usual positivity condition is not needed. We present several examples with a finite set of vertices and study the performance of the estimator on simulated data from theses examples. We also
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21

Dharmagunawardhana, Chathurika. "Image texture analysis based on Gaussian Markov Random Fields." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/372489/.

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Texture analysis is one of the key techniques of image understanding and processing with widespread applications from low level image segmentation to high level object recognition. Gaussian Markov random field (GMRF) is a particular model based texture feature extraction scheme which uses model parameters as texture features. In this thesis a novel robust texture descriptor based on GMRF is proposed specially for texture segmentation and classification. For these tasks, descriptive features are more favourable relative to the generative features. Therefore, in order to achieve more descriptive
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22

Islam, 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.

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We propose a novel approach to investigate and implement unsupervised segmentation of color images particularly natural color images. The aim is to devise a robust unsu- pervised segmentation approach that can segment a color textured image accurately. Here, the color and texture information of each individual pixel along with the pixel's spatial relationship within its neighborhood have been considered for producing precise segmentation of color images. Precise segmentation of images has tremendous potential in various application domains like bioinformatics, forensics, security and surveilla
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23

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.

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We propose a novel approach to investigate and implement unsupervised segmentation of color images particularly natural color images. The aim is to devise a robust unsu- pervised segmentation approach that can segment a color textured image accurately. Here, the color and texture information of each individual pixel along with the pixel's spatial relationship within its neighborhood have been considered for producing precise segmentation of color images. Precise segmentation of images has tremendous potential in various application domains like bioinformatics, forensics, security and surveilla
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24

Chandgotia, Nishant. "Markov random fields and measures with nearest neighbour Gibbs potential." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37000.

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This thesis will discuss the relationship between stationary Markov random fields and probability measures with a nearest neighbour Gibbs potential. While the relationship has been well explored when the measures are fully supported, we shall discuss what happens when we weaken this assumption.
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25

Milun, 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.

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26

Caputo, 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.

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<p>Recognizing 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 rea
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27

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.

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<p>The 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
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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.

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Markov Random Fields (MRFs) are used in computer vision as an effective method for reconstructing a function starting from a set of noisy, or sparse data, or in the integration of early vision processes to label physical discontinuities. The MRF formalism is attractive because it enables the assumptions used to be explicitly stated in the energy function. The drawbacks of such models have been the computational complexity of the implementation, and the difficulty in estimating the parameters of the model. In this thesis, the deterministic approximation to the MRF models derived by Girosi and
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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.

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30

Wehmann, 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.

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31

Toftaker, 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.

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<p>Discrete 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
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32

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.

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The essence of the interest rate modeling by using Heath-Jarrow-Morton framework is to find the drift condition of the instantaneous forward rate dynamics so that the entire term structure is arbitrage free. In this study, instantaneous forward interest rates are modeled using random fields and Markov Jump processes and the drift conditions of the forward rate dynamics are given. Moreover, the methodology presented in this study is extended to certain financial settings and instruments such as multi-country interest rate models, term structure of defaultable bond prices and forward measures. A
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Caputo, 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.

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Muffert, 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.

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Dai, Zhenwen, and 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.

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Dai, 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.

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Minin, 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.

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Fauske, 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.

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<p>In 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
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Villanueva-Morales, Antonio. "Modified pseudo-likelihood estimation for Markov random fields with Winsorized Poisson conditional distributions." [Ames, Iowa : Iowa State University], 2008.

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Tang, 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.

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Shakya, 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.

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Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability distribution from population of solutions, and sample it to generate the next population. Many EDAs use probabilistic graphical modelling techniques for this purpose. In particular, directed graphical models (Bayesian networks) have been widely used in EDA. This thesis proposes an undirected graphical model (Markov Random Field (MRF)) approach to e
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Wang, 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.

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<p> A 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 pot
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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.

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La segmentación de imágenes es una técnica que tiene como finalidad dividir una imagen en un conjunto de regiones, asignando a cada objeto en la escena una o varias regiones. Para obtener una segmentación correcta, cada una de las regiones debe cumplir con un criterio de homogeneidad impuesto a priori. Cuando se fija un criterio de homogeneidad, lo que implícitamente se esta haciendo es asumir un modelo matemático que caracteriza las regiones.<br/><br/>En esta tesis se introduce un nuevo tipo de modelo denominado modelo jerárquico, ya que tiene dos niveles diferentes sobrepuestos uno sobre el
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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.

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Kim, 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.

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Zhang, 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.

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Liang, 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.

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A long term record of the earth's vegetation is important in studies of global climate change. Over the last three decades, multiple data sets on vegetation have been collected using different satellite-based sensors. There is a need for methods that combine these data into a long term earth system data record. The Advanced Very High Resolution Radiometer (AVHRR) has provided reflectance measures of the entire earth since 1978. Physical and statistical models have been used to improve the consistency and reliability of this record. The Moderated Resolution Imaging Spectroradiometer (MODIS) has
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Zhu, 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.

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Thesis (Ph. D.)--Washington State University, December 2008.<br>Title from PDF title page (viewed on Sept. 23, 2008) "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 83-90).
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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.

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There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening
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Kutarnia, 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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" A novel Markov Random Field (MRF) based method for the mosaicing of 3D ultrasound volumes is presented in this dissertation. The motivation for this work is the production of training volumes for an affordable ultrasound simulator, which offers a low-cost/portable training solution for new users of diagnostic ultrasound, by providing the scanning experience essential for developing the necessary psycho-motor skills. It also has the potential for introducing ultrasound instruction into medical education curriculums. The interest in ultrasound training stems in part from the widespread adopt
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