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dc.contributor.authorDeborah, Hilda
dc.contributor.authorRichard, Noel
dc.contributor.authorHardeberg, Jon Yngve
dc.date.accessioned2020-01-20T12:49:02Z
dc.date.available2020-01-20T12:49:02Z
dc.date.created2019-11-28T17:58:16Z
dc.date.issued2019
dc.identifier.citationProceedings of SPIE, the International Society for Optical Engineering. 2019, 11172 .nb_NO
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11250/2637025
dc.description.abstractThe development of a spectral difference-based statistical processing of hyperspectral images is provided in this article. Kullback-Leibler pseudo-divergence function, which was specifically developed for the metrological processing of hyperspectral images, is used at the foundation of the statistics. As a demonstration of its use, the proposed statistics are used in visualising surface variability within a set of pigment patches. It is then further exploited to detect anomalies and deterioration that occur on the patches.nb_NO
dc.language.isoengnb_NO
dc.publisherSociety of Photo-optical Instrumentation Engineers (SPIE)nb_NO
dc.titleApplication of spectral statistics to surface defect detectionnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber8nb_NO
dc.source.volume11172nb_NO
dc.source.journalProceedings of SPIE, the International Society for Optical Engineeringnb_NO
dc.identifier.doi10.1117/12.2521714
dc.identifier.cristin1754093
dc.description.localcode© 2019 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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