On the Applications of Robust PCA in Image and Video Processing

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing, and 3-D computer vision. Indeed, most of the time these applications require to detect sparse ou...

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Vydáno v:	Proceedings of the IEEE Ročník 106; číslo 8; s. 1427 - 1457
Hlavní autoři:	Bouwmans, Thierry, Javed, Sajid, Zhang, Hongyang, Lin, Zhouchen, Otazo, Ricardo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	3-D computer vision Algorithms Computer Science Computer vision Data analysis Face recognition Image detection Image Processing Magnetic resonance imaging Matrix decomposition Medical imaging NMR Noise reduction Nuclear magnetic resonance Object recognition Outliers (statistics) Principal component analysis Principal components analysis Recovery Restoration robust principal component analysis (RPCA) Robustness Sparse matrices Statistical analysis Three dimensional motion Video video processing Video process-ing Robust PCA 3D Computer Vision Medical Imaging Image processing
ISSN:	0018-9219, 1558-2256
On-line přístup:	Získat plný text
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