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|Title:||Low rank and sparse matrix reconstruction with partial support knowledge for surveillance video processing||Authors:||Zonoobi, D.
partially known support
Robust Principal Component Analysis
|Issue Date:||2013||Citation:||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|
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