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 55 for “"Gaussian mixture models"”.
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Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations
… of sparse observational data with computational models so as to optimally improve the probabilistic description of the field of interest, thereby reducing uncertainties. The centerpiece of this thesis is the introduction of a novel such scheme that overcomes prior shortcomings observed within the …
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Improving Computation for Hierarchical Bayesian Spatial Gaussian Mixture Models with Application to the Analysis of THz image of Breast Tumor
… <p>The third chapter starts with an overview of Gaussian mixture models (GMMs). However, because in the GMM framework the observations are assumed to be independent, GMMs are less effective when the mixture data exhibits spatial autocorrelation. To improve the performance of GMMs on …
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A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video
… segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are …
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Técnicas para conversão de orador em sinais de voz
… techniques, three from the literature, based on Gaussian mixture models, hidden Markov models and feed forward neural networks, and one novel based on recurrent neural networks, were evaluated. Two methods to generate the excitation used in the synthesis step were also implemented, one utilizing …
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A Minimax Approach for Learning Gaussian Mixtures
… distributionlearning benchmarks including Gaussian mixture models (GMMs). In this thesis, we propose Generative Adversarial Training for Gaussian Mixture Models (GATGMM), a minimax GAN framework for learning GMMs. Motivated by optimal transport theory, we design the zero-sum game in GAT-GMM …
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A Bayesian classification framework with label corrections
… modeling. The same framework not only works on Gaussian mixture models, but it’s also universally applicable on top of any parametric or non-parametric method, such as the kernel method and Dirichlet Process (DP) priors. With a thorough study of the kernel and Dirichlet Process method, we …
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Hierarchical density estimation for image classification
"Histogram (bag-of-words) and Gaussian mixture models (GMMs) have been widely used in patch-based image classification problems. Despite the satisfactory results reported, both methods suffer from a number of disadvantages. For instance, a histogram may be easy to learn but has a large quantization …
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Statistical and algorithmic foundation of K-means clustering
… statistical guarantees under the standard Gaussian mixture models in that it achieves an information-theoretic bound for exact recovery. However, the original SDP method is limited to isotropic covariance matrices for Gaussians, and it has prohibitively high costs of solving the SDP …
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Advanced Statistical Modeling for Model-Based Iterative Reconstruction for Single-Energy and Dual-Energy X-Ray CT
… scans, each requiring refined statistical models including the data model and the prior model. In this dissertation, we developed an MBIR algorithm for dual-energy CT that included a joint data-likelihood model to account for correlated data noise. Moreover, we developed a Gaussian-Mixture …
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Model-based cardiac CT segmentation and 3D heart reconstruction
… thesis proposes a fast marching method driven by Gaussian mixture models (GMM) to segment hepatic vein from CT images. Anisotropic smoothing is applied to the original CT data to remove the noise. After that, GMMs are built for both hepatic vein area and non-hepatic vein areas based on hand-draw …
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Image-based facial expression recognition
… Vector Quantization and Supervised Hierarchical Gaussianization algorithms. The key idea in both of these is to iteratively update the Gaussian Mixture Models in such a way that they are more suitable for classification in these respective frameworks. We then present some exciting results for …
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Automatic Detection of Landmark Acoustic Cues in Human Speech
… speech-related measurements and training Gaussian Mixture Models (GMMs). To remove the effects of speaker variability and different recording environments, methods for normalizing speech-related measurements are proposed and evaluated. For a new speech signal, the normalized speech-related …
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PASSIVE RADAR TRACK CLUSTERING: HIGHER FIDELITY OF TARGET IDENTIFICATION AND CLASSIFICATION OF UNLABELED TRACKS
… We implemented SSL pipelines using K–Means and Gaussian Mixture Models (GMMs) to generate pseudo-labels from limited labeled data for classifier training. The Max Hold Spectrogram performs the best for both SL and USL approaches. GMMs outperform K–Means for all unsupervised clustering …
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Resource-Aware Distributed Particle Filtering for Cluster-Based Object Tracking in Wireless Camera Networks
… information among nodes: synchronized particles, Gaussian mixture models, and Parzen windows. We show that all three approaches benefit from the proposed resource-aware mechanism in terms of tracking accuracy or energy efficiency.</p>
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Machine Learning to Predict Warhead Fragmentation In-Flight Behavior from Static Data
… imaging technique and simulation data combined. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to …
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Bayesian Relevance for Enhanced Human-Robot Collaboration
… Current human intent prediction methods, such as Gaussian Mixture Models and Conditional Random Fields, are generally less interpretable due to their lack of causality between variables. A novel framework called Bayesian Relevance (BR) is presented for human intent prediction in HRC scenarios. The …
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Partitioned particle filtering for target tracking in video sequences
… on a per-pixel basis, using sixteen-centered Gaussian Mixture Models trained on the available colour information for each target. Assumptions about the behaviour of each pixel allow for the improvement under certain circumstances of the basic pixel classification by smoothing, using Hidden …
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Processing Methods for the Detection of Landmark Acoustic Cues
… indicators of certain acoustic cues. Finally, Gaussian Mixture Models using the selected raw and processed measurements are trained in order to efficiently and accurately distinguish landmark cues. These steps are applied to Vowel and Glide landmarks to develop a module that can distinguish …
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Energy-Efficient Real-Time Hardware Acceleration for Gaussian Fitting
… memory accesses. A promising approach involving Gaussian Mixture Models (GMMs), Single-Pass Gaussian Fitting (SPGF) algorithm, allowed for real-time 3D mapping with minimal memory and energy requirements due to its single-pass processing of input data. To further decrease demonstrated energy …
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