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Separating background and foregroundin video based on a nonparametric Bayesian model

Publication ,  Journal Article
Ding, X; Carin, L
Published in: IEEE Workshop on Statistical Signal Processing Proceedings
September 5, 2011

Separating background and foreground in video is a fundamental problem in computer vision. We present a Bayesian hierarchical model to address this challenge, and apply it to video with dynamic scenes. The model uses a nonparametric prior, a beta-bernoulli process, for both the background and foreground representation. Additionally, the model uses neighborhood information of each pixel to encourage group clustering of the foreground. A collapsed Gibbs sampler is used for efficient posterior inference. Experimental results show competitive performance of the proposed model. © 2011 IEEE.

Duke Scholars

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

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Publication Date

September 5, 2011

Start / End Page

321 / 324
 

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Ding, X., & Carin, L. (2011). Separating background and foregroundin video based on a nonparametric Bayesian model. IEEE Workshop on Statistical Signal Processing Proceedings, 321–324. https://doi.org/10.1109/SSP.2011.5967692
Ding, X., and L. Carin. “Separating background and foregroundin video based on a nonparametric Bayesian model.” IEEE Workshop on Statistical Signal Processing Proceedings, September 5, 2011, 321–24. https://doi.org/10.1109/SSP.2011.5967692.
Ding X, Carin L. Separating background and foregroundin video based on a nonparametric Bayesian model. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;321–4.
Ding, X., and L. Carin. “Separating background and foregroundin video based on a nonparametric Bayesian model.” IEEE Workshop on Statistical Signal Processing Proceedings, Sept. 2011, pp. 321–24. Scopus, doi:10.1109/SSP.2011.5967692.
Ding X, Carin L. Separating background and foregroundin video based on a nonparametric Bayesian model. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;321–324.

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

September 5, 2011

Start / End Page

321 / 324