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dc.contributor.authorAmani, Mahdi
dc.contributor.authorFalk, Håvard Hagen
dc.contributor.authorJensen, Oliver Damsgaard
dc.contributor.authorVartdal, Gunnar
dc.contributor.authorAune, Anders
dc.contributor.authorLindseth, Frank
dc.date.accessioned2020-01-30T12:47:05Z
dc.date.available2020-01-30T12:47:05Z
dc.date.created2020-01-16T10:54:21Z
dc.date.issued2019
dc.identifier.isbn978-3-030-34995-0
dc.identifier.urihttp://hdl.handle.net/11250/2638887
dc.description.abstractMany recent medical developments rely on image analysis, however, it is not convenient nor cost-efficient to use professional image acquisition tools in every clinic or laboratory. Hence, a reliable color calibration is necessary; color calibration refers to adjusting the pixel colors to a standard color space. During a real-life project on neonatal jaundice disease detection, we faced a problem to perform skin color calibration on already taken images of neonatal babies. These images were captured with a smartphone (Samsung Galaxy S7, equipped with a 12 Mega Pixel camera to capture 4032 ×× 3024 resolution images) in the presence of a specific calibration pattern. This post-processing image analysis deprived us from calibrating the camera itself. There is currently no comprehensive study on color calibration methods applied to human skin images, particularly when using amateur cameras (e.g. smartphones). We made a comprehensive study and we proposed a novel approach for color calibration, Gaussian process regression (GPR), a machine learning model that adapts to environmental variables. The results show that the GPR achieves equal results to state-of-the-art color calibration techniques, while also creating more general models.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleColor Calibration on Human Skin Imagesnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber13nb_NO
dc.identifier.doihttps://doi.org/10.1007/978-3-030-34995-0_20
dc.identifier.cristin1774588
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article. Locked until 23.11.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-34995-0_20nb_NO
cristin.unitcode194,63,10,0
cristin.unitcode194,65,20,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.unitnameInstitutt for samfunnsmedisin og sykepleie
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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