scholarly journals Packet Video Error Concealment With Gaussian Mixture Models

2008 ◽  
Vol 17 (2) ◽  
pp. 145-154 ◽  
Author(s):  
D. Persson ◽  
T. Eriksson ◽  
P. Hedelin
Keyword(s):  
Mixture Models ◽  
Error Concealment ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Packet Video ◽  
Video Error Concealment

A robust non-rigid point set registration method based on inhomogeneous Gaussian mixture models

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme

2013 ◽  
Vol 141 (6) ◽  
pp. 1737-1760 ◽  
Author(s):  
Thomas Sondergaard ◽  
Pierre F. J. Lermusiaux
Keyword(s):  
Data Assimilation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Planck Equation ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Field Equations ◽  
Dynamical Equations ◽  
Prior Probabilities

Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

scholarly journals Novelty detection and segmentation based on Gaussian mixture models: A case study in 3D robotic laser mapping

2013 ◽  
Vol 61 (12) ◽  
pp. 1696-1709 ◽  
Author(s):  
Paulo Drews ◽  
Pedro Núñez ◽  
Rui P. Rocha ◽  
Mario Campos ◽  
Jorge Dias
Keyword(s):  
Mixture Models ◽  
Novelty Detection ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture
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