Sparse Background Modeling
Sparse background modeling approaches can be classified as follows:
Compressive Sensing (CS) (23 papers)
Bayesian Compressive Sensing (BCS) (4 papers)
Distributed Compressive Sensing (DCS) (1 paper)
Structured Sparsity (SS) (3 papers)
Group Sparsity (GS) (5 papers)
Dictionary Learning (DL) (23 papers)
Dictionary Vectors (22 papers)
Dictionary Filters (1 paper)
Sparse Error Estimation (7 papers)
Linear Approximation (3 papers)
Linear Regression (4 papers)
Manifold Sparse Representation (1 paper)
Dynamic Mode Decomposition (15 papers)
Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. Rochelle, France.
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As this website gives many information that come from my research, please cite my following survey papers:
T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014. [pdf]
T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]
T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey", Recent Patents on Computer Science, Volume 4, No. 3, pages147-176, September 2011. [pdf]
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