1- Robust Subspace Learning

Handbook on Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing,  CRC Press, Taylor and Francis Group,  May 2016.

Book on Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications,  Academic Press, Elsevier, June 2017.

Low-Rank Matrix Recovery and Completion via Convex Optimization (Perception and Decision Lab.,  University of Illinois, USA)

DLAM website (T. Bouwmans, Lab. MIA, Univ. La Rochelle, France)

LRS Library  (A. Sobral, L3i, Univ. La Rochelle, France)

The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used or adapted for other computer vision. Currently the LRSLibrary contains a total of 72 matrix-based and tensor-based algorithms. The LRSLibrary was tested successfully in MATLAB R2013b both x86 and x64 versions.

2- Dynamic Mode Decomposition

Book on Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, SIAM, 2016.

3-Compressive Sensing

Compressive Sensing Resources (Rice University, USA)

4-Background/Foreground Separation

Handbook on Background Modeling and Foreground Detection for Video Surveillance, CRC Press, Taylor and Francis Group, July 2014.

Background Subtraction Website (T. Bouwmans, Lab. MIA, Univ. La Rochelle, France)

(See the Section « Recent Background Modeling » for Background Modeling via Robust Subspace Learning via Decomposition into Low-rank plus Additive Matrices)

BGS Library  (A. Sobral, L3i, Univ. La Rochelle, France)

The BGSLibrary  provides an easy-to-use C++ framework based on OpenCV to perform background subtraction (BGS) in videos. The BGSLibrary compiles under Linux, Mac OS X and Windows. The source code is available under GNU GPL v3 license, the library is free and open source for academic purposes.

 5-Structure for Motion

VisualSFM (C. Wu, Google, USA)

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM).