Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression
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[1] Gongguo Tang,et al. Robust principal component analysis based on low-rank and block-sparse matrix decomposition , 2011, 2011 45th Annual Conference on Information Sciences and Systems.
[2] I. Daubechies,et al. Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.
[3] Kentaro Toyama,et al. Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[4] Laura Balzano,et al. Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Qi Tian,et al. Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.
[6] John Wright,et al. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.
[7] L. Dixon,et al. Finite Algorithms in Optimization and Data Analysis. , 1988 .
[8] John Wright,et al. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.
[9] Zhixun Su,et al. Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation , 2011, NIPS.
[10] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[11] Lucia Maddalena,et al. A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection , 2010, Neural Computing and Applications.
[12] Shiqian Ma,et al. Algorithms for sparse and low-rank optimization: convergence, complexity and applications , 2011 .