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 94 for “"Gaussian mixture model"”.
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An improved Gaussian mixture model algorithm for background subtraction
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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The Single Imputation Technique in the Gaussian Mixture Model Framework
… imputation technique in the basic regression model: the main motivation is that, the residual is added to improve the bias and variability. The residual is drawn by normal distribution assumption with a mean of 0, and the variance is equal to the residual variance. Although new methods in the …
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People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization
… In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are …
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A New Breast Cancer Image Classifier Using Gaussian Mixture Model Based on Histogram and Enhanced Roughness Index
… In this thesis, a new method based on Gaussian Mixture Model (GMM) to perform the breast tumor classification into two different classes (benign class and malignant class) was proposed. Also a new Enhance Roughness Index (ERI) was developed. In the meanwhile, the two different factors …
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Molecular Code Division Multiple Access: Gaussian Mixture Modeling
… other in the same environment. A new channel model and detection technique along with a molecular-based access method, are proposed in here for communication between asynchronous users. In this work, the received molecular signal is modeled as a Gaussian mixture distribution when the MC system …
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Parallel and distributed MCMC inference using Julia
… on large datasets. Being able to eciently learn models on large datasets holds the future of machine learning. As the speed of serial computation stalls, it is necessary to utilize the power of parallel computing in order to better scale with the growing complexity of algorithms and the growing …
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as …
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Feature extraction and normalization in SVM speaker verification using telephone speech
… and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to environmental noise, distortions from transmission over communication channels such as the telephone channel, and intersession variability.
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Interactive Imaging via Hand Gesture Recognition.
… in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample …
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Bayesian inference of stochastic dynamical models
… for Bayesian inference of stochastic dynamical models is developed. The methodology leverages the dynamically orthogonal (DO) evolution equations for reduced-dimension uncertainty evolution and the Gaussian mixture model DO filtering algorithm for nonlinear reduced-dimension state variable …
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Robust Event Detection and Retrieval in Surveillance Video
… pointing gesture. We use an improved adaptive Gaussian mixture model for background modeling and foreground detection; a connected component labeling algorithm is then employed to label the foreground pixels. A Kalman filter approach is used to build models for the entities of interest (people …
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… to be sparse, inducing a sparsely connected Gaussian graph. In the nonparametric Mixture of Factor Analyzers, the covariance matrices in the Gaussian Mixture Model are forced to be low-rank, which is closely related to the concept of block sparsity. </p><p>Finally in the information-theoretic …
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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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Location Verification for Spoofing Detection in Non-Terrestrial Networks
… function for the unknown user position using a Gaussian mixture model and employ a likelihood ratio decision rule for location verification. Results display receiver operating characteristic curves to evaluate the LVS performance under various satellite ephemeris error conditions, spoofing …
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Vocal modulation features in the prediction of major depressive disorder severity
This thesis develops a model of vocal modulations up to 50 Hz in sustained vowels as a basis for biomarkers of neurological disease, particularly Major Depressive Disorder (MDD). Two model components contribute to amplitude modulation (AM): AM from respiratory muscles and from interaction between …
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Investigating Neuronal Cell Classes and their Role in Cognition
… memory task, and employing an unsupervised Gaussian mixture model (GMM) clustering algorithm, a number of different cell classes and their defining features were distinguished in area 7A, the lateral intraparietal area (LIP), the dorsolateral and ventrolateral prefrontal cortex (PFC) and the …
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A study of computational methods to analyze gene expression data
… proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with …
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In-home Gait Health Monitoring using Machine Learning and Ambient Sensing
… gait asymmetry is estimated using an adapted gaussian mixture model approach. Third, the footfalls are localized through a tracking machine learning approach. Lastly, the tibial acceleration is estimated using machine learning. Experiments were performed to evaluate the performance of these …
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Use of Computer Vision to Track Thin Body Motion with the Application of Tracking Passion Plant Vine Tendrils
… (SIFT), and temporal based segmentation using Gaussian Mixture Model Background Subtraction to segment out the tendril in each video frame. Morphological image processing methods, such as dilation and connected com- ponent analysis, were used to clean up the segmentation results to give an …
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Renewable Energy Integration in Distribution System with Artificial Intelligence
… with an Open System Interconnection (OSI) model to the data visualization platform with a high-speed communication architecture. Google Earth and Global Geographic Information System (GIS) are used to design the visualization platform and realize the results.</p> <p>Based on the data …
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