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 65 for “"Markov random field"”.
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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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COMBINING MARKOV RANDOM FIELD AND MARKED POINT PROCESS FOR MICROSCOPY IMAGE MODELING
In many microscopy image analysis applications, it is of critical importance to address
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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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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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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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Three essays on agricultural risk and insurance
… approaches, a geo-statistical approach and a Markov random field approach, are compared. The Markov random field approach is preferred because it has a smaller cross-validation prediction mean squared error. A temperature index insurance is presented based on interpolated data. The potential …
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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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Social Interaction Inference and Group Emergent Leadership Detection Using Head Pose
… in a group conversation scenario using the Markov Random Field model. A novel interaction feature is proposed to represent the transactional segment using the head pose. Furthermore, I extend the approach to infer the hierarchical structure of a group with the contextual information, which …
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Dropped object detection in crowded scenes
… that the scene is being captured through a mid-field static camera, our approach consists of segmenting the foreground from the background and then using a change analyzer to detect any objects which meet certain criteria. In this thesis, we describe a background model and a method of …
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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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Information theoretic approach for assessing image fidelity in photon-counting imagers
… source and photon-counted images is derived in a Markov random field setting and normalized by the source-image's entropy, yielding a fidelity metric that is between zero and unity, which respectively corresponds to complete loss of information and full preservation of information. Calculations …
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Autonomous altitude estimation of a miniature helicopter using a single onboard camera.
… set of altitudes. Finally, a spatio-temporal Markov Random Field is modeled over the altitudes in test images, which is maximized over the posterior distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a …
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Clustering Partition Models for Discrete Structures with Applications in Geographical Epidemiology
… considered as well as regular lattices. The main field of application is geographical epidemiology. In this thesis a prior model for the use within a hierarchical Bayesian framework is developed, and a theoretical basis is given. The proposed partition model combines the units under investigation …
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Bayesian analysis for mixtures of discrete distributions with a non-parametric component
… experiments is shown. A one-dimensional Markov random field model is proposed, which accounts for the spatial dependencies in the data. The methodology is also applied to ChIP-seq data, which shows that the new method detected more genes enriched regions than similar existing methods at …
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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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Concurrency Optimization for Integrative Network Analysis
… cancers. Chen et. al. developed a bagging Markov random field (BMRF)-based approach which examines gene expression data with prior biological information to reliably identify significant genes and proteins. Using random resampling with replacement (bootstrapping or bagging) is essential to …
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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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