Results 211 to 220 of about 64,158 (253)

A Robust Multivariate Thresholding Function for Sparse and Biomedical Signal Reconstruction. [PDF]

Sensors (Basel)
Ullah H, Gaire S, Graves CA.
europepmc   +1 more source

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models. [PDF]

Anal Sci
Kaneko H.
europepmc   +1 more source

Product-of-Gaussian-mixture diffusion models for joint nonlinear MRI reconstruction. [PDF]

J Math Imaging Vis
Nagler L, Zach M, Pock T.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Fuzzy Gaussian Mixture Models

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhaojie Ju, Honghai Liu
exaly   +3 more sources

Splitting Gaussians in Mixture Models

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker ...
Rubén Heras Evangelio, Michael Pätzold, Thomas Sikora   +2 more
openaire   +1 more source

Hierarchical Gaussian mixture model

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified.
Vincent Garcia, Frank Nielsen, Richard Nock   +2 more
openaire   +1 more source

Parsimonious Gaussian mixture models

Statistics and Computing, 2008
Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases.
Paul David McNicholas, Thomas Brendan Murphy   +1 more
openaire   +1 more source

Modelling profiles with a mixture of Gaussians

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002
Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be
James Orwell, Darrel Greenhill, Jonathan D. Rymel, Graeme A. Jones   +3 more
openaire   +1 more source

Combining Gaussian Mixture Models

2004
A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters ...
Hyoungjoo Lee, Sungzoon Cho
openaire   +1 more source

Detection in underwater noises modeled as a Gaussian-Gaussian mixture

ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
We study statistical modeling by a Gaussian-Gaussian mixture for two different underwater noise samples. We show that one of them can be adequately described by a Gaussian-Gaussian mixture whereas the other one is very close to a Gaussian model and is described by a mixture with a very small perturbating term.
Michel Bouvet, Stuart C. Schwartz
openaire   +1 more source