Gaussian mixture model for video compressive sensing

Jianbo Yang; Xin Yuan; Xuejun Liao; Patrick Llull; Guillermo Sapiro; David J. Brady; Lawrence Carin

doi:10.1109/ICIP.2013.6738005

Gaussian mixture model for video compressive sensing

Jianbo Yang, Xin Yuan, Xuejun Liao, Patrick Llull, Guillermo Sapiro, David J. Brady, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

Abstract

A Gaussian Mixture Model (GMM)-based algorithm is proposed for video reconstruction from temporal compressed measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The developed GMM reconstruction method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed GMM with videos reconstructed from simulated compressive video measurements and from a real compressive video camera. © 2013 IEEE.

Original language	English (US)
Title of host publication	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages	19-23
Number of pages	5
DOIs	https://doi.org/10.1109/ICIP.2013.6738005
State	Published - Dec 1 2013
Externally published	Yes

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10.1109/ICIP.2013.6738005

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title = "Gaussian mixture model for video compressive sensing",

abstract = "A Gaussian Mixture Model (GMM)-based algorithm is proposed for video reconstruction from temporal compressed measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The developed GMM reconstruction method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed GMM with videos reconstructed from simulated compressive video measurements and from a real compressive video camera. {\textcopyright} 2013 IEEE.",

author = "Jianbo Yang and Xin Yuan and Xuejun Liao and Patrick Llull and Guillermo Sapiro and Brady, {David J.} and Lawrence Carin",

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AU - Yuan, Xin

AU - Liao, Xuejun

AU - Llull, Patrick

AU - Sapiro, Guillermo

AU - Brady, David J.

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2013/12/1

Y1 - 2013/12/1

N2 - A Gaussian Mixture Model (GMM)-based algorithm is proposed for video reconstruction from temporal compressed measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The developed GMM reconstruction method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed GMM with videos reconstructed from simulated compressive video measurements and from a real compressive video camera. © 2013 IEEE.

AB - A Gaussian Mixture Model (GMM)-based algorithm is proposed for video reconstruction from temporal compressed measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The developed GMM reconstruction method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed GMM with videos reconstructed from simulated compressive video measurements and from a real compressive video camera. © 2013 IEEE.

UR - http://ieeexplore.ieee.org/document/6738005/

UR - http://www.scopus.com/inward/record.url?scp=84897680640&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2013.6738005

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M3 - Conference contribution

SN - 9781479923410

SP - 19

EP - 23

BT - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

ER -

Gaussian mixture model for video compressive sensing

Abstract

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