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 proceedingConference contribution

19 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.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages19-23
Number of pages5
ISBN (Print)9781479923410
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: Sep 15 2013Sep 18 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period9/15/139/18/13

Keywords

  • Compressive sensing
  • Gaussian mixture model
  • coded aperture compressive temporal imaging

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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