Survey Approach

Regular Subspace Learning

Journal

T. Bouwmans, “Subspace Learning for Background Modeling: A Survey”, Recent Patents on Computer Science, Volume 2, No 3, pages 223-234, November 2009

Robust PCA via L+S Decomposition

Chapters

T. Bouwmans, E. Zahzah,"Robust Principal Component Analysis via Decomposition into Low-rank and Sparse Matrices: An overview", Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May 2016.

C. Guyon, T. Bouwmans, E. Zahzah, “Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis”, INTECH, Principal Component Analysis, Book 1, Chapter 12, page 223-238, March 2012.

Journals

T. Bouwmans, A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, Volume 23, pages 1-71, February 2017.

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.

RPCA and Dynamic RPCA via L+S Decomposition

Journal

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Subspace Learning: Robust PCA, Robust Subspace Tracking and Robust Subspace Recovery”, IEEE Signal Processing Magazine, Volume 35, No. 4, pages 32-55, July 2018.

Conference

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust PCA and Robust Subspace Tracking: A Comparative Evaluation”, IEEE Statistical Signal Processing Workshop,

SSP 2018, Freiburg, Germany, June 2018.