Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2013.6738069
Title: Low rank and sparse matrix reconstruction with partial support knowledge for surveillance video processing
Authors: Zonoobi, D.
Kassim, A.A. 
Keywords: Compressive sampling
partially known support
Robust Principal Component Analysis
Video Compression
Issue Date: 2013
Source: Zonoobi, D.,Kassim, A.A. (2013). Low rank and sparse matrix reconstruction with partial support knowledge for surveillance video processing. 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings : 335-339. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2013.6738069
Abstract: It has been shown recently that incorporating priori knowledge into the basic compressive sensing results in significant improvement of its performance. This has motivated us to extend the incorporation of partial known support into the problem of Robust Principal Component Analysis (RPCA) from compressive measurements. Our proposed algorithm utilizes the known part of the support to recover a matrix as the sum of a low-rank matrix and a sparse component and is tested on the problem of surveillance video reconstruction from compressive measurements. Our experimental results show that the incorporation of partial known support, can significantly improve the reconstruction performance of video sequences. © 2013 IEEE.
Source Title: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/83905
ISBN: 9781479923410
DOI: 10.1109/ICIP.2013.6738069
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Feb 21, 2018

Page view(s)

33
checked on Feb 24, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.