TY - GEN
T1 - Separating background and foregroundin video based on a nonparametric Bayesian model
AU - Ding, Xinghao
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/9/5
Y1 - 2011/9/5
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/5967692/
UR - http://www.scopus.com/inward/record.url?scp=80052207627&partnerID=8YFLogxK
U2 - 10.1109/SSP.2011.5967692
DO - 10.1109/SSP.2011.5967692
M3 - Conference contribution
SN - 9781457705700
SP - 321
EP - 324
BT - IEEE Workshop on Statistical Signal Processing Proceedings
ER -