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Video compressive sensing using Gaussian mixture models.

Publication ,  Journal Article
Yang, J; Yuan, X; Liao, X; Llull, P; Brady, DJ; Sapiro, G; Carin, L
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
November 2014

A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.

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Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

November 2014

Volume

23

Issue

11

Start / End Page

4863 / 4878

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 1702 Cognitive Sciences
  • 0906 Electrical and Electronic Engineering
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Yang, J., Yuan, X., Liao, X., Llull, P., Brady, D. J., Sapiro, G., & Carin, L. (2014). Video compressive sensing using Gaussian mixture models. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 23(11), 4863–4878. https://doi.org/10.1109/tip.2014.2344294
Yang, Jianbo, Xin Yuan, Xuejun Liao, Patrick Llull, David J. Brady, Guillermo Sapiro, and Lawrence Carin. “Video compressive sensing using Gaussian mixture models.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 23, no. 11 (November 2014): 4863–78. https://doi.org/10.1109/tip.2014.2344294.
Yang J, Yuan X, Liao X, Llull P, Brady DJ, Sapiro G, et al. Video compressive sensing using Gaussian mixture models. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2014 Nov;23(11):4863–78.
Yang, Jianbo, et al. “Video compressive sensing using Gaussian mixture models.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 23, no. 11, Nov. 2014, pp. 4863–78. Epmc, doi:10.1109/tip.2014.2344294.
Yang J, Yuan X, Liao X, Llull P, Brady DJ, Sapiro G, Carin L. Video compressive sensing using Gaussian mixture models. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2014 Nov;23(11):4863–4878.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

November 2014

Volume

23

Issue

11

Start / End Page

4863 / 4878

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 1702 Cognitive Sciences
  • 0906 Electrical and Electronic Engineering
  • 0801 Artificial Intelligence and Image Processing