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 27 for “"Markov Random Field (MRF)"”.
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Color Image Segmentation Using the Bee Algorithm in the Markovian Framework
… analysis in computer vision. A survey of the Markov Random Field (MRF) with four different implementation methods for its parameter estimation is provided. In addition, a survey of swarm intelligence and a number of swarm based algorithms are presented. The MRF model is used for color image …
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StructureTransfer: A Scene Parsing Framework via Graph Matching for Images and Point Clouds
… scores, are introduced into a traditional Markov Random Field (MRF) for the inference. The parsing accuracy of the proposed method is close to state-of-the-art algorithms on images. With the point clouds, the accuracy is significantly enhanced. The proposed framework shows remarkable …
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Advanced spatial information processes: modeling and application
… of the state of a two-dimensional discrete Markov Random Field (MRF). The implementation of the recursive algorithm is a form of dynamic programming. The second framework is based on a stochastic relaxation algorithm and Markov-Gibbs Random Fields. The relaxation algorithm constitutes an …
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Energy-efficient information inference in wireless sensor networks based on graphical modeling
… In our approach, we develop a pairwise Markov Random Field (MRF) to model the spatial correlations in a sensor network. Our MRF model is first constructed through automatic learning from historical sensed data, by using Iterative Proportional Fitting (IPF). When the MRF model is …
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Analysis and synthesis of iris images
… content of the iris image thereby resulting in a Markov Random Field (MRF) model for the iris image. This information is expected to be useful for the development of user-specific models for iris images, i.e. the matcher could be tuned to accommodate the characteristics of each user's iris image …
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A Graphical Model for the Communications Channel
… novel channel model that is based on a Gaussian Markov random field (MRF) for the complex channel gains. This graphical model is used to capture the local nature of the statistical dependencies (in time and space) of the channel taps. In order for the MRF model to fit the actual physical channel …
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fMRI detection with spatial regularization
… We take advantage of these models and apply a Markov Random Field (MRF) spatial prior to the statistics provided by the voxel-by-voxel algorithms. MRF has been shown to be able to overcome the effect of over-smoothing, which is the major drawback of the conventional spatial regularization …
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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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Inter-annual stability of land cover classification: explorations and improvements
… year of data. Second, I use a spatio-temporal Markov Random Field (MRF) model to post-process the predictions of a per-pixel classifier. The MRF framework reduces spurious label change to a level comparable to that achieved by a post-hoc heuristic stabilization technique. The timing of label …
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Approximate inference : decomposition methods with applications to networks
Markov random field (MRF) model provides an elegant probabilistic framework to formulate inter-dependency between a large number of random variables. In this thesis, we present a new approximation algorithm for computing Maximum a Posteriori (MAP) and the log-partition function for arbitrary …
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Computational Reconstruction and Quantification of Aerospace Materials
… data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the …
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Automatisches Segmentieren von Mikroarraybildern
… protocols are highly useful. In this work a Markov random field (MRF) based approach to high level grid segmentation is proposed, which is robust to common problems encountered with array images and does not require calibration. The MRF framework allows to separate the heuristic modeling of …
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Improve OpenMVG and create a novel algorithm for novel view synthesis from point clouds
… for the novel view by solving a multi-label Markov Random Field (MRF) optimization problem using graph-cuts. We introduce a novel energy minimization formulation exploits both 2D and 3D information. Finally, a photorealistic image of the novel view is rendered by copying pixel colors from …
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Compression and Reliable Transmission of Digital Image and Video Signals
… models for image subband data. We present a Markov Random Field (MRF) model for this data as well as an algorithm for, given a set of observations, identifying parameters of an MRF likely to have generated the observations. Image subbands have been empirically found not to behave like iid …
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Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation
… for noisy point clouds in the plane based on a Markov random field (MRF) optimization that we call Point Cloud Densitybased Segmentation (PCDS). We also developed a large synthetic dataset of in plane point clouds that includes either a set of randomly placed, sized and oriented primitive …
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DNA microarray image processing based on advanced pattern recognition techniques
… foreground from background. In addition, markov random field (MRF), as well as, a proposed wavelet based MRF model (SMRF) were implemented. The segmentation abilities of all the algorithms were evaluated by means of the segmentation matching factor (SMF), the Coefficient of Determination …
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Markov random field modeling of the spatial distribution of proteins on cell membranes
… can be observed simultaneously. As a solution, Markov random field (MRF) modeling is proposed to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with …
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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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New marked point process models for microscopy images
… the amount of image data keeps increasing in the fields such as materials science and biomedical engineering. As a result, image processing plays a critical role in this era of science and technology. In materials image analysis, image segmentation and feature detection are considered very …
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A population Monte Carlo approach to estimating parametric bidirectional reflectance distribution functions through Markov random field parameter estimation
… for an object surface. The method uses a novel Markov Random Field (MRF) formulation on triplets of corner vertex nodes to model the probability of sets of reflectance parameters for arbitrary reflectance models, given probabilistic surface geometry, camera, illumination, and reflectance image …
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