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Relevant bibliographies by topics / Mixture of Gaussian (MoG)

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Academic literature on the topic 'Mixture of Gaussian (MoG)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Mixture of Gaussian (MoG)"

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Kim, Yongho. "Fast MOG (Mixture of Gaussian) Algorithm based on Predicting Model Parameters." TECHART: Journal of Arts and Imaging Science 2, no. 1 (2015): 41. http://dx.doi.org/10.15323/techart.2015.02.2.1.41.

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Kong, Long, Symeon Chatzinotas, and Bjorn Ottersten. "Unified Framework for Secrecy Characteristics With Mixture of Gaussian (MoG) Distribution." IEEE Wireless Communications Letters 9, no. 10 (2020): 1625–28. http://dx.doi.org/10.1109/lwc.2020.2999361.

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Matczak, Grzegorz, and Przemyslaw Mazurek. "Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection." Remote Sensing 13, no. 5 (2021): 870. http://dx.doi.org/10.3390/rs13050870.

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Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Ne

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Wang, Biao, Chunhao Chen, Zhe Jiang, and Yu Zhao. "ROV State Estimation Using Mixture of Gaussian Based on Expectation-Maximization Cubature Particle Filter." Applied Sciences 13, no. 10 (2023): 5885. http://dx.doi.org/10.3390/app13105885.

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The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF conside

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Fatima, Ezzahra Sloukia, Bouarfa Rajae, Medromi Hicham, and Wahbi Mohammed. "BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE." International Journal of Network Security & Its Applications (IJNSA) 5, no. 3 (2013): 85–97. https://doi.org/10.5281/zenodo.4326213.

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Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently publi

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Setiawan, Ariyono, I. Gede Susrama Mas Diyasa, Moch Hatta, and Eva Yulia Puspaningrum. "Mixture gaussian V2 based microscopic movement detection of human spermatozoa." International Journal of Advances in Intelligent Informatics 6, no. 2 (2020): 210. http://dx.doi.org/10.26555/ijain.v6i2.507.

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Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compa

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Yao, Li, and Miaogen Ling. "An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/424050.

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Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very cro

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Bhaggiaraj., S., Kumar. C. Ranjeeth, Vijay. K. S. Rahul, and Prabhu. A. Vignesh. "Vehicle Surveillance and Tracking using Background Segmentation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 164–69. https://doi.org/10.35940/ijeat.F1310.089620.

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A significant initial step for video investigation is Background Subtraction and it is utilized to find the objects of enthusiasm for additional prerequisites. Foundation deduction approach is a general technique for movement recognition strategy, which proficiently utilizes the distinction of the current picture and the foundation picture to recognize moving articles. Here the proposed calculation is known as Mixture of Gaussian (MOG) process. This goes under a quality investigation calculation for pictures, which could be handled in the recordings and casings. A methodology is utilized along

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N., Satish Kumar, and G. Shobha. "HYBRID APPROACH FOR KEY FRAME EXTRACTION FROM VIDEO SEQUENCE." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 4 RACSIT (2017): 97–104. https://doi.org/10.5281/zenodo.583896.

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This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.

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Cao, Xiangyong, Zongben Xu, and Deyu Meng. "Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field." Remote Sensing 11, no. 13 (2019): 1565. http://dx.doi.org/10.3390/rs11131565.

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In this paper, a new supervised classification algorithm which simultaneously considers spectral and spatial information of a hyperspectral image (HSI) is proposed. Since HSI always contains complex noise (such as mixture of Gaussian and sparse noise), the quality of the extracted feature inclines to be decreased. To tackle this issue, we utilize the low-rank property of local three-dimensional, patch and adopt complex noise strategy to model the noise embedded in each local patch. Specifically, we firstly use the mixture of Gaussian (MoG) based low-rank matrix factorization (LRMF) method to s

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Dissertations / Theses on the topic "Mixture of Gaussian (MoG)"

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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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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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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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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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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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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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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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Books on the topic "Mixture of Gaussian (MoG)"

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1st, Krishna M. Vamsi. Brain Tumor Segmentation Using Bivariate Gaussian Mixture Models. Selfypage Developers Pvt Ltd, 2022.

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Gaussian Mixture Reduction for Tracking Multiple Maneuvering Targets in Clutter. Storming Media, 2003.

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Speaker Verification in the Presence of Channel Mismatch Using Gaussian Mixture Models. Storming Media, 1997.

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Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

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Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions o

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Anomaly Detection Using a Variational Autoencoder Neural Network with a Novel Objective Function and Gaussian Mixture Model Selection Technique. Independently Published, 2019.

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Book chapters on the topic "Mixture of Gaussian (MoG)"

1

Yu, Dong, and Li Deng. "Gaussian Mixture Models." In Automatic Speech Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_2.

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Sarang, Poornachandra. "Gaussian Mixture Model." In Thinking Data Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-02363-7_11.

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Reynolds, Douglas. "Gaussian Mixture Models." In Encyclopedia of Biometrics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_196.

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Reynolds, Douglas. "Gaussian Mixture Models." In Encyclopedia of Biometrics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_196.

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Aylward, Stephen, and Stephen Pizer. "Continuous Gaussian mixture modeling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63046-5_14.

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Liu, Honghai, Zhaojie Ju, Xiaofei Ji, Chee Seng Chan, and Mehdi Khoury. "Fuzzy Gaussian Mixture Models." In Human Motion Sensing and Recognition. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53692-6_5.

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Bourrier, Anthony, Rémi Gribonval, and Patrick Pérez. "Compressive Gaussian Mixture Estimation." In Compressed Sensing and its Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16042-9_8.

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Scrucca, Luca, Chris Fraley, T. Brendan Murphy, and Adrian E. Raftery. "Visualizing Gaussian Mixture Models." In Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003277965-6.

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Lee, Hyoung-joo, and Sungzoon Cho. "Combining Gaussian Mixture Models." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_98.

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Meconcelli, Duccio, and Edmondo Trentin. "Gaussian-Mixture Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71602-7_2.

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Conference papers on the topic "Mixture of Gaussian (MoG)"

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Rudić, Branislav, Markus Pichler-Scheder, and Dmitry Efrosinin. "Valid Decoding in Gaussian Mixture Models." In 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS). IEEE, 2024. https://doi.org/10.1109/citds62610.2024.10791365.

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Semeraro, Simone, and Keith A. LeGrand. "Gaussian Mixture Based Progressive Chernoff Fusion." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706440.

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Giraldo-Grueso, Felipe, Andrey A. Popov, and Renato Zanetti. "Gaussian Mixture-Based Point Mass Filtering." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706279.

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Kumagai, Naoya, and Kenshiro Oguri. "Chance-Constrained Gaussian Mixture Steering to a Terminal Gaussian Distribution." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886105.

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Kong, Long, Jiguang He, Yun Ai, Symeon Chatzinotas, and Bjorn Ottersten. "Effective Rate Evaluation with Assistance of Mixture Gamma (MG), Mixture of Gaussian (MoG), and Fox’s H-Function Distributions." In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, 2021. http://dx.doi.org/10.1109/vtc2021-spring51267.2021.9448819.

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Tabkhi, Hamed, Majid Sabbagh, and Gunar Schirner. "A Power-Efficient FPGA-Based Mixture-of-Gaussian (MoG) Background Subtraction for Full-HD Resolution." In 2014 IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2014. http://dx.doi.org/10.1109/fccm.2014.76.

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Tabkhi, Hamed, Robert Bushey, and Gunar Schirner. "Algorithm and architecture co-design of Mixture of Gaussian (MoG) background subtraction for embedded vision." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810615.

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Chen, Zitai, Chuan Chen, Zong Zhang, Zibin Zheng, and Qingsong Zou. "Variational Graph Embedding and Clustering with Laplacian Eigenmaps." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/297.

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As a fundamental machine learning problem, graph clustering has facilitated various real-world applications, and tremendous efforts had been devoted to it in the past few decades. However, most of the existing methods like spectral clustering suffer from the sparsity, scalability, robustness and handling high dimensional raw information in clustering. To address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and Clustering with Laplacian Eigenmaps (VGECLE), which learns node embeddings and assigns node clusters simultaneously. It represents each node as a

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Pang, John Z. F., Hong Cao, and Vincent Y. F. Tan. "MOGT: Oversampling with a parsimonious mixture of Gaussian trees model for imbalanced time-series classification." In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. http://dx.doi.org/10.1109/mlsp.2013.6661937.

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Fronzeo, Melissa A., Michael Kinzel, and Jules Lindau. "Artificially Ventilated Cavities: Evaluating the Constant-Pressure Approximation." In ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69367.

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Computational Fluid Dynamics (CFD) is employed to study the fundamental aspects of the internal pressure within artificially ventilated, gaseous cavities in both twin- and toroidal-vortex closure modes. The results show that several pressure regions develop within the cavities, indicating that the common assumption that the cavity has a constant pressure breaks down when evaluated in high detail. The internal cavity pressure is evaluated using a probability density function (PDF). The resulting PDF plots show a clusters with multiple peaks. A mixture-of-Gaussians (MOG) method is employed to be

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Reports on the topic "Mixture of Gaussian (MoG)"

1

Yu, Guoshen, and Guillermo Sapiro. Statistical Compressive Sensing of Gaussian Mixture Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540728.

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Gardiner, Thomas, and Allen Robinson. Gaussian Mixture Model Solvers for the Boltzmann Equation. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/2402991.

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De Leon, Phillip L., and Richard D. McClanahan. Efficient speaker verification using Gaussian mixture model component clustering. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1039402.

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Hogden, J., and J. C. Scovel. MALCOM X: Combining maximum likelihood continuity mapping with Gaussian mixture models. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/677150.

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Gerlach, K. R. Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada352456.

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Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540722.

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Clendinen, Chaevien, Javier Flores, Lisa Bramer, David Degnan, Vanessa Paurus, and Yuri Eberlim de Corilo. Enter Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1985304.

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Lee, Jhong S., Leonard E. Miller, Robert H. French, and Young K. Kim. Ocean Surveillance Detection Studies. Part 1. Detection in Gaussian Mixture Noise. Part 2. An Investigation of Canonical Correlation as an Automatic Detection and Beamforming Technique. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160931.

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Ramakrishnan, Aravind, Ashraf Alrajhi, Egemen Okte, Hasan Ozer, and Imad Al-Qadi. Truck-Platooning Impacts on Flexible Pavements: Experimental and Mechanistic Approaches. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-038.

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Truck platoons are expected to improve safety and reduce fuel consumption. However, their use is projected to accelerate pavement damage due to channelized-load application (lack of wander) and potentially reduced duration between truck-loading applications (reduced rest period). The effect of wander on pavement damage is well documented, while relatively few studies are available on the effect of rest period on pavement permanent deformation. Therefore, the main objective of this study was to quantify the impact of rest period theoretically, using a numerical method, and experimentally, using

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