DLAT Website

The aim of this web site is to provide resources such as references (254 papers), codes (1 code) and links to demonstration websites (1 website) for the research on decomposition into low-rank plus additive tensors by grouping all related researches and particularly recent advances in this field.

T . Bouwmans, "DLAT Website", Laboratoire MIA, Univ. of La Rochelle, June 2016.

A full overview of the different decompositions in low-rank plus additive matrices are provided in:

Editors: T. Bouwmans, N. Aybat, E. Zahzah. Title: Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing"

Publisher : CRC Press, Taylor and Francis Group.

Publication Date : May 30, 2016. (More information) [Purchase]

Fair Use Policy

This web site presents a survey on decomposition in low-rank plus additive tensors. If you use information from this web site for publication, please cite the following papers:

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. [pdf]

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. [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]

Note: My publications are available on Academia, Research Gate, Researchr, ORCID and Publication List.