Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 52 for “"Markov Random Fields"”.
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Greedy structure learning of Markov Random Fields
… of an undirected graphical model, also called a Markov Random Field (MRF), given a set of independent and identically distributed (i.i.d.) samples. Specifically, we introduce an adaptive forward-backward greedy algorithm for learning the structure of a discrete, pairwise MRF given a high …
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Optimization of Markov Random Fields in Computer Vision
… of computer vision tasks can be formulated using Markov Random Fields (MRF). Except in certain special cases, optimizing an MRF is intractable, due to a large number of variables and complex dependencies between them. In this thesis, we present new algorithms to perform inference in MRFs, that are …
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Optimization of Markov Random Fields in Computer Vision
… of computer vision tasks can be formulated using Markov Random Fields (MRF). Except in certain special cases, optimizing an MRF is intractable, due to a large number of variables and complex dependencies between them. In this thesis, we present new algorithms to perform inference in MRFs, that are …
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Lossless Coding of Markov Random Fields with Complex Cliques
The topic of Markov Random Fields (MRFs) has been well studied in the past, and has found practical use in various image processing, and machine learning applications. Where coding is concerned, MRF specific schemes have been largely unexplored. In this thesis, an overview is given of recent …
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A GPU implementation of tiled belief propagation on Markov random fields
In this work, we present a parallelized version of tiled belief propagation for stereo matching. The proposed algorithm is implemented in CUDA to leverage parallel processing capabilities of GPUs. In our solution, the original tiled BP algorithm is combined with a number of optimizations specific …
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DEUM: a framework for an estimation of distribution algorithm based on Markov random fields.
… thesis proposes an undirected graphical model (Markov Random Field (MRF)) approach to estimate and sample the distribution in EDAs. The interaction between variables in the solution is modelled as an undirected graph and the joint probability of a solution is factorised as a Gibbs distribution. …
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Estimation of GMRFs by recursive cavity modeling
… approach to approximate inference in large Gauss-Markov random fields. The main idea is to recursively dissect the field, constructing a cavity model for each subfield at each level of dissection. The cavity model provides a compact yet (nearly) faithful model for the surface of one subfield …
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Contributions to modeling and computer efficient estimation for Gaussian space -time processes
… class is capable of modeling smooth spatial random fields. We show that, for rectangular lattices, this class is equivalent to higher-order Markov random fields. Thus we capture the computational advantage of iterative updating of Markov random fields, while at the same time provide the …
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Models for Yield Estimation of Multichip Module Ceramic Substrates
… formation. Our defect geometry model employs Markov random fields to represent more general defect shapes. Yield is estimated using Monte Carlo sampling methods. Finally, we present methods for estimating the parameters of the yield models using observed data from the manufacturing process.
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Sparse Matrix Belief Propagation
… which executes loopy belief propagation in Markov random fields by replacing indexing over graph neighborhoods with sparse-matrix operations. This abstraction allows for seamless integration with optimized sparse linear algebra libraries, including those that perform matrix and tensor …
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Markov random field image modelling
… form of parametric model for the distribution. Markov random fields (MRFs) provide just such a vehicle for modelling the a priori distribution of labels found in images. In particular, this work investigated the suitability of MRF models for modelling a priori information about the distribution …
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Learning from data: Plant breeding applications of machine learning
… and <em>in silico</em>. We used mixed models and Markov random fields to evaluate phenotypic-genotypic-environmental associations among traits and learning properties of genome-wide prediction methods. Alternative methods for analyses were proposed.</p>
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Scalable Structure Learning of Graphical Models
… two popular models: dynamical linear systems and Markov random fields. For each case, we investigate major computational bottlenecks of baseline learning techniques. Next, we propose two frameworks that provide higher scalability using appropriate problem reformulation and efficient structure …
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Recognizing deviations from normalcy for brain tumor segmentation
… between the layers via multi-level Markov random fields or iterated Bayesian classification. A simple instantiation of the framework has been implemented to perform preliminary experiments on synthetic and MRI data.
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Approximate inference in graphical models using LP relaxations
Graphical models such as Markov random fields have been successfully applied to a wide variety of fields, from computer vision and natural language processing, to computational biology. Exact probabilistic inference is generally intractable in complex models having many dependencies between the …
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Cutting plane algorithms for variational inference in graphical models
… for probabilistic inference in discrete Markov Random Fields (MRFs). Valid constraints are derived for the marginal polytope through a series of projections onto the cut polytope. Projecting onto a larger model gives an efficient separation algorithm for a large class of valid …
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A Hardware Generator for Factor Graph Applications
… decoding of forward error correcting codes, Markov chains and Markov Random Fields, Kalman Filtering, Fourier Transforms, and even some games such as Sudoku. In this paper, a framework is presented for rapid prototyping of hardware implementations of FG-based applications. The FG developer …
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Machine learning of image analysis with convolutional networks and topological constraints
… convolutional networks are closely related to Markov random fields (MRFs), a popular probabilistic approach to image analysis, but can in practice can achieve significantly greater model complexity. The second aspect of our approach is the use of domain specific cost functions and learning …
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Ανάπτυξη συστήματος επεξεργασίας δεδομένων τηλεπισκόπησης για αυτόματη ανίχνευση και ταξινόμηση περιοχών με περιβαλλοντικές αλλοιώσεις
… ανάλυσης και ταξινόμησης υφής όπως GLCM, Markov Random Fields, Gabor Filters και Neural Networks. Η μελέτη προσανατολίστηκε στην ανάπτυξη νέων μεθόδων ταξινόμησης υφής για αυξημένη αποτελεσματικότητα. Χρησιμοποιήθηκαν δεδομένα πολυφασματικά και SAR. Για τα πολυφασματικά δεδομένα προτάθηκε …
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Belief propagation generative adversarial networks
… proposed framework produces samples by passing random inputs through a neural network to construct the local potentials in the graphical model; performing probabilistic inference in this graphical model then yields the marginal distribution. Message passing based on discrete variables keeps a …
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