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Rozprawy doktorskie na temat „Mixture of Gaussian (MoG)”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Medasani, Swarup. "Robust algorithms for mixture decomposition with application to classification, boundary description, and image retrieval /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9904860.

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Kunkel, Deborah Elizabeth. "Anchored Bayesian Gaussian Mixture Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524134234501475.

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Nkadimeng, Calvin. "Language identification using Gaussian mixture models." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4170.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various

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4

Gundersen, Terje. "Voice Transformation based on Gaussian mixture models." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10878.

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<p>In this thesis, a probabilistic model for transforming a voice to sound like another specific voice is tested. The model is fully automatic and only requires some 100 training sentences from both speakers with the same acoustic content. The classical source-filter decomposition allows prosodic and spectral transformation to be performed independently. The transformations are based on a Gaussian mixture model and a transformation function suggested by Y. Stylianou. Feature vectors of the same content from the source and target speaker, aligned in time by dynamic time warping, are fitted to a

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Subramaniam, Anand D. "Gaussian mixture models in compression and communication /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112847.

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6

Dahmen, Jörg. "Invariant image object recognition using Gaussian mixture densities." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=964586940.

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7

Cilliers, Francois Dirk. "Tree-based Gaussian mixture models for speaker verification." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019.1/1639.

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8

Robbiati, Stefano Andrea. "Sequential Gaussian mixture techniques for target tracking applications." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/11886.

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9

Lu, Liang. "Subspace Gaussian mixture models for automatic speech recognition." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8065.

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In most of state-of-the-art speech recognition systems, Gaussian mixture models (GMMs) are used to model the density of the emitting states in the hidden Markov models (HMMs). In a conventional system, the model parameters of each GMM are estimated directly and independently given the alignment. This results a large number of model parameters to be estimated, and consequently, a large amount of training data is required to fit the model. In addition, different sources of acoustic variability that impact the accuracy of a recogniser such as pronunciation variation, accent, speaker factor and en

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10

Pinto, Rafael Coimbra. "Continuous reinforcement learning with incremental Gaussian mixture models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157591.

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A contribução original desta tese é um novo algoritmo que integra um aproximador de funções com alta eficiência amostral com aprendizagem por reforço em espaços de estados contínuos. A pesquisa completa inclui o desenvolvimento de um algoritmo online e incremental capaz de aprender por meio de uma única passada sobre os dados. Este algoritmo, chamado de Fast Incremental Gaussian Mixture Network (FIGMN) foi empregado como um aproximador de funções eficiente para o espaço de estados de tarefas contínuas de aprendizagem por reforço, que, combinado com Q-learning linear, resulta em performance com

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11

Lan, Jing. "Gaussian mixture model based system identification and control." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014640.

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12

Parra, Vásquez Gabriel Enrique. "Spectral mixture kernels for Multi-Output Gaussian processes." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/150553.

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Magíster en Ciencias de la Ingeniería, Mención Matemáticas Aplicadas. Ingeniero Civil Matemático<br>Multi-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression problem by modeling the correlation in time and/or space (as scalar GPs do), but also across channels and thus revealing statistical dependencies among different sources of data. This is crucial in a number of real-world applications such as fault detection, data imputation

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13

Vakil, Sam. "Gaussian mixture model based coding of speech and audio." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81575.

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The transmission of speech and audio over communication channels has always required speech and audio coders with reasonable search and computational complexity and good performance relative to the corresponding distortion measure.<br>This work introduces a coding scheme which works in a perceptual auditory domain. The input high dimensional frames of audio and speech are transformed to power spectral domain, using either DFT or MDCT. The log spectral vectors are then transformed to the excitation domain. In the quantizer section the vectors are DCT transformed and decorrelated. This op

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14

Sadarangani, Nikhil 1979. "An improved Gaussian mixture model algorithm for background subtraction." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87293.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaves 71-72).<br>by Nikhil Sadarangani.<br>M.Eng.

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15

Chang, Hung-An Ph D. Massachusetts Institute of Technology. "Large-margin Gaussian mixture modeling for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44367.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Includes bibliographical references (p. 101-103).<br>Discriminative training for acoustic models has been widely studied to improve the performance of automatic speech recognition systems. To enhance the generalization ability of discriminatively trained models, a large-margin training framework has recently been proposed. This work investigates large-margin training in detail, integrates the training with more flexible classifier structures such as hierarchical classifiers and

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16

Stuttle, Matthew Nicholas. "A gaussian mixture model spectral representation for speech recognition." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620077.

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17

Wang, Juan. "Estimation of individual treatment effect via Gaussian mixture model." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/839.

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In this thesis, we investigate the estimation problem of treatment effect from Bayesian perspective through which one can first obtain the posterior distribution of unobserved potential outcome from observed data, and then obtain the posterior distribution of treatment effect. We mainly consider how to represent a joint distribution of two potential outcomes - one from treated group and another from control group, which can give us an indirect impression of correlation, since the estimation of treatment effect depends on correlation between two potential outcomes. The first part of this thesis

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18

Reynolds, Douglas A. "A Gaussian mixture modeling approach to text-independent speaker identification." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/16903.

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19

Delport, Marion. "A spatial variant of the Gaussian mixture of regressions model." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/65883.

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In this study the nite mixture of multivariate Gaussian distributions is discussed in detail including the derivation of maximum likelihood estimators, a discussion on identi ability of mixture components as well as a discussion on the singularities typically occurring during the estimation process. Examples demonstrate the application of the nite mixture of univariate and bivariate Gaussian distributions. The nite mixture of multivariate Gaussian regressions is discussed including the derivation of maximum likelihood estimators. An example is used to demonstrate the application of the mixt

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20

Sinha, Kaushik. "New Directions in Gaussian Mixture Learning and Semi-supervised Learning." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001.

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21

Chockalingam, Prakash. "Non-rigid multi-modal object tracking using Gaussian mixture models." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937467/.

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Thesis (M.S.) -- Clemson University, 2009.<br>Contains additional supplemental files. Title from first page of PDF file. Document formatted into pages; contains vii, 54 p. ; also includes color graphics.

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22

Marek, Petr. "Gaussian mixtures in R." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193077.

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Using Gaussian mixtures is a popular and very flexible approach to statistical modelling. The standard approach of maximum likelihood estimation cannot be used for some of these models. The estimates are, however, obtainable by iterative solutions, such as the EM (Expectation-Maximization) algorithm. The aim of this thesis is to present Gaussian mixture models and their implementation in R. The non-trivial case of having to use the EM algorithm is assumed. Existing methods and packages are presented, investigated and compared. Some of them are extended by custom R code. Several exhaustive simu

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23

Wang, Bo Yu. "Deterministic annealing EM algorithm for robust learning of Gaussian mixture models." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493309.

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Plasse, Joshua H. "The EM Algorithm in Multivariate Gaussian Mixture Models using Anderson Acceleration." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/290.

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Over the years analysts have used the EM algorithm to obtain maximum likelihood estimates from incomplete data for various models. The general algorithm admits several appealing properties such as strong global convergence; however, the rate of convergence is linear which in some cases may be unacceptably slow. This work is primarily concerned with applying Anderson acceleration to the EM algorithm for Gaussian mixture models (GMM) in hopes of alleviating slow convergence. As preamble we provide a review of maximum likelihood estimation and derive the EM algorithm in detail. The iterates that

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25

Soares, Sérgio Aurélio Ferreira. "Spatial interpolation and geostatistic simulation with the incremental Gaussian mixture network." reponame:Repositório Institucional da UFSC, 2016. https://repositorio.ufsc.br/xmlui/handle/123456789/178581.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2016.<br>Made available in DSpace on 2017-08-22T04:22:16Z (GMT). No. of bitstreams: 1 347911.pdf: 1690914 bytes, checksum: e43f9150ef3cb130f6d5696b46a68fa5 (MD5) Previous issue date: 2016<br>Abstract : Geostatistics aggregates a set of tools designed to deal with spatially correlated data. Two significant problems that Geostatistics tackles are the spatial interpolation and geostatistical simulation. Kriging and Sequential Gaussian Simulatio

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26

Sadeghi, Mohammad T. "Automatic architecture selection for probability density function estimation in computer vision." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843248/.

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In this thesis, the problem of probability density function estimation using finite mixture models is considered. Gaussian mixture modelling is used to provide a semi-parametric density estimate for a given data set. The fundamental problem with this approach is that the number of mixtures required to adequately describe the data is not known in advance. In this work, a predictive validation technique [91] is studied and developed as a useful, operational tool that automatically selects the number of components for Gaussian mixture models. The predictive validation test approves a candidate mo

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27

Brand, Rinus. "A comparison of Gaussian mixture variants with application to automatic phoneme recognition." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/594.

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28

MENDES, EDUARDO FONSECA. "MODELING NONLINEAR TIME SERIES WITH A TREE-STRUCTURED MIXTURE OF GAUSSIAN MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9689@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Neste trabalho um novo modelo de mistura de distribuições é proposto, onde a estrutura da mistura é determinada por uma árvore de decisão com transição suave. Modelos baseados em mistura de distribuições são úteis para aproximar distribuições condicionais desconhecidas de dados multivariados. A estrutura em árvore leva a um modelo que é mais simples, e em alguns casos mais interpretável, do que os propostos anteriormente na literatura. Baseando-se no algoritmo de Esperança- Maximização (EM), foi derivado um estimador de qu

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29

Sondergaard, Thomas S. M. Massachusetts Institute of Technology. "Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68954.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 177-180).<br>Data assimilation, as presented in this thesis, is the statistical merging 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 community. Adopting techniques preval

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30

Tran, Denis. "A study of bit allocation for Gaussian mixture model quantizers and image coders /." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=83937.

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This thesis describes different bit allocation schemes and their performances when applied on coding line spectral frequencies (LSF) using the GMM-based coder designed by Subramaniam and a simple image transform coder. The new algorithms are compared to the original bit allocation formula; the Pruning algorithm used by Subramaniam, Segall's method and the Greedy bit allocation algorithm using the Log Spectral Distortion and the Mean-Square Error for the LSF quantizer and the Peak Signal-to-Noise Ratio for the image coder.<br>First, a Greedy level allocation algorithm is developed based o

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31

Flores, João Henrique Ferreira. "ARMA-CIGMN : an Incremental Gaussian Mixture Network for time series analysis and forecasting." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/116126.

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Este trabalho apresenta um novo modelo de redes neurais para análise e previsão de séries temporais: o modelo ARMA-CIGMN (do inglês, Autoregressive Moving Average Classical Incremental Gaussian Mixture Network) além dos resultados obtidos pelo mesmo. Este modelo se baseia em modificações realizadas em uma versão reformulada da IGMN. A IGMN Clássica, CIGMN, é similar à versão original da IGMN, porém baseada em uma abordagem estatística clássica, a qual também é apresentada neste trabalho. As modificações do algoritmo da IGMN foram feitas para melhor adpatação a séries temporais. O modelo ARMA-C

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32

Diaz, Jorge Cristhian Chamby. "An incremental gaussian mixture network for data stream classification in non-stationary environments." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174484.

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Classificação de fluxos contínuos de dados possui muitos desafios para a comunidade de mineração de dados quando o ambiente não é estacionário. Um dos maiores desafios para a aprendizagem em fluxos contínuos de dados está relacionado com a adaptação às mudanças de conceito, as quais ocorrem como resultado da evolução dos dados ao longo do tempo. Duas formas principais de desenvolver abordagens adaptativas são os métodos baseados em conjunto de classificadores e os algoritmos incrementais. Métodos baseados em conjunto de classificadores desempenham um papel importante devido à sua modularidade,

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33

Safont, Armero Gonzalo. "New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/53913.

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[EN] This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability

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34

Pradella, Lorenzo. "A data-driven prognostic approach based on AR identification and hidden Markov models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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In this work a data-driven prognostic approach based on AutoRegressive (AR) estimation and hidden Markov models (HMMs) is addressed. In particular, the approach is capable of achieving Prognostic and Health Management (PHM) tasks such as real time detection and Remaining Useful Life (RUL) estimation. The approach can be seen as composed of a training part (offline) and an exploitation part (online). The offline part relies upon the use of a scalar health indicator coming from the system identification field: the Itakura Saito (IS) spectral distance. In particular, raw acceleration data, gathe

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35

Kam, Po-ling, and 甘寶玲. "Mixture autoregression with heavy-tailed conditional distribution." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29614922.

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36

Shashidhar, Sanda, and Amirisetti Sravya. "Online Handwritten Signature Verification System : using Gaussian Mixture Model and Longest Common Sub-Sequences." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15807.

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Moradiannejad, Ghazaleh. "People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24304.

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Tracking multiple articulated objects (such as a human body) and handling occlusion between them is a challenging problem in automated video analysis. This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. 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 b

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38

Webb, Grayson. "A Gaussian Mixture Model based Level Set Method for Volume Segmentation in Medical Images." Thesis, Linköpings universitet, Beräkningsmatematik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148548.

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This thesis proposes a probabilistic level set method to be used in segmentation of tumors with heterogeneous intensities. It models the intensities of the tumor and surrounding tissue using Gaussian mixture models. Through a contour based initialization procedure samples are gathered to be used in expectation maximization of the mixture model parameters. The proposed method is compared against a threshold-based segmentation method using MRI images retrieved from The Cancer Imaging Archive. The cases are manually segmented and an automated testing procedure is used to find optimal parameters f

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Chowdhury, Tashnim Jabir Shovon. "A distributed cooperative algorithm for localization in wireless sensor networks using Gaussian mixture modeling." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1481227449382602.

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Lindström, Kevin. "Fault Clustering With Unsupervised Learning Using a Modified Gaussian Mixture Model and Expectation Maximization." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176535.

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When a fault is detected in the engine, the check engine light will come on. After that, it is often up to the mechanic to diagnose the engine fault. Manual fault classification by a mechanic can be time-consuming and expensive. Recent technological advancements have granted us immense computing power, which can be utilized to diagnose faults using data-driven classifiers. Data-driven classifiers generally require a lot of training data to be able to accurately diagnose system faults by comparing sensor data to training data because labeled training data is required for a wide variety of diffe

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Wang, Mike M. Eng Massachusetts Institute of Technology. "Product perceptual mapping on fashion designs with Gaussian mixture variational autoencoder and triplet loss." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121642.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 51-53).<br>Product perceptual maps are visualizations of the perceptions of products by customers. They provide many advantages to businesses, such as identifying gaps in the market, understanding competition, and finding how

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42

Casey, Patrick John. "Real-time estimation of MIG welding weld bead width using an IR camera." Thesis, 2009. http://hdl.handle.net/2152/ETD-UT-2009-08-257.

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Current manufacturing process controls are principally based only on statistical performance. The next evolution is to make physics based models combined with the state of the art sensors and actuators to control the manufacturing processes. In this paper, metal inert gas welding is used as an example of how the ﬁrst steps in developing a reliable estimation technique to implement a physics based controller. The weld bead geometry will be the main focus because it is crucial to creating a quality weld. This paper uses an IR camera to generate and evaluate multiple weld bead width estimation te

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Sung, Hsi Guang. "Gaussian mixture regression and classification." Thesis, 2004. http://hdl.handle.net/1911/18710.

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The sparsity of high dimensional data space renders standard nonparametric methods ineffective for multivariate data. A new procedure, Gaussian Mixture Regression (GMR), is developed for multivariate nonlinear regression modeling. GMR has the tight structure of a parametric model, yet still retains the flexibility of a nonparametric method. The key idea of GMR is to construct a sequence of Gaussian mixture models for the joint density of the data, and then derive conditional density and regression functions from each model. Assuming the data are a random sample from the joint pdf fX,Y, we fit

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44

Xu, Yeong-Yuh, and 徐永煜. "The Study of Mixture Gaussian Neural Networks." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/51314093930021132212.

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博士<br>國立交通大學<br>資訊工程系所<br>92<br>In this dissertation, the mixture Gaussian Neural networks are proposed for pattern recognition. {\it Self-growing Probabilistic Decision based Neural Network} (SPDNN) is proposed to classify the numerical data. The ISLUG training scheme is introduced to tune the SPDNN parameters to improve the classification accuracy. Furthermore, a generalized version of SPDNN, called {\it Generalized Probabilistic decision based Neural Network} (GPDNN), is proposed to handle the general case that the data are in the form of the distributions instead of the numerical quantitie

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Hsu, Jui-Hsiang, and 許瑞雄. "Moving Object Detection Using Gaussian Mixture Models." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/80953997432270297389.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>95<br>This thesis consists of three parts. In the first part, the background subtraction method is introduced and the background update algorithm and the threshold method required in background subtraction are discussed. Next, a dynamic background image model based on the Gaussian Mixture Model method is introduced. In the approach, the model parameters are estimated by using Gaussian distributions. The gradient statistical information for each pixel is utilized to separate background and foreground. Although this method can effectively update the parameters and skip

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Chen, Chia-Hua, and 陳甲樺. "Parsimonious Gaussian Mixture Modelling With Missing Information." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/47500077644198226181.

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碩士<br>國立中興大學<br>應用數學系所<br>99<br>Celeux and Govaert (1995, Pattern Recognition, 28, pp. 781-793) presented a new class of Gaussian mixture models (GMM) in which the within-group covariance matrices are structured parsimoniously in a geo -metrically interpretable way as originally introduced by Banfield and Raftery (1993, Biometics, 49, pp. 803-821). In this thesis, we establish computation -ally flexible EM-type algorithms for parameter estimation of ten parsi -monious forms of GMM under missing at radom mechanism. For the ease of computation and theoretical developments, two auxiliary indicato

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Łuszczkiewicz-Piątek, Maria. "Gaussian mixture models for color image retrieval." Rozprawa doktorska, 2009. https://repolis.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=8008.

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Łuszczkiewicz-Piątek, Maria. "Gaussian mixture models for color image retrieval." Rozprawa doktorska, 2009. https://delibra.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=8008.

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Lai, Chu-Shiuan, and 賴竹煖. "Gaussian Mixture of Background and Shadow Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/38760050190895130218.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>98<br>In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adapti

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Sue, Yung-Chun, and 蘇詠鈞. "Specified Gestures Identification using Gaussian Mixture Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/15219540833691164661.

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碩士<br>清雲科技大學<br>電子工程所<br>100<br>Sign language recognition technique is composed by the hand images detection and the hand gestures recognition. Hand images detection is locating the sign language select, sign language capture, the palm and fingers part from the sensed image, and rotating them to the appropriate hand posture, both are the important pre-processing for sign language identification and recognition. This paper first introduced sequentially throughout the study practices, as well as the process of image pre-processing instructions. The major work in the hand gestures recognition is

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