|Title||Gaussian mixture model for video compressive sensing|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||J Yang, X Yuan, X Liao, P Llull, G Sapiro, DJ Brady, and L Carin|
|Journal||2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings|
|Pagination||19 - 23|
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.
|Short Title||2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings|