Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/14128
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dc.contributor.authorLi, Y-
dc.contributor.authorXu, L-Q-
dc.contributor.authorMorphett, J-
dc.contributor.authorJacobs, R-
dc.date.accessioned2017-02-24T12:13:33Z-
dc.date.available2003-
dc.date.available2017-02-24T12:13:33Z-
dc.date.issued2003-
dc.identifier.citationProceedings of IEEE International Conference on Image Processing, (ICIP), 14-17 September, 1: pp. 245 - 248, (2003)en_US
dc.identifier.isbn0-7803-7750-8-
dc.identifier.issn1522-4880-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/14128-
dc.description.abstractPrincipal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.en_US
dc.format.extent245 - 248-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.sourceIEEE International Conference on Image Processing-
dc.sourceIEEE International Conference on Image Processing-
dc.subjectPrincipal component analysisen_US
dc.subjectImage processingen_US
dc.subjectEigenvalues and eigenfunctionsen_US
dc.subjectPattern recognitionen_US
dc.titleAn integrated algorithm of incremental and robust PCAen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICIP.2003.1246944-
pubs.volume1-
Appears in Collections:Dept of Computer Science Research Papers

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