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dc.contributor.authorGigilashvili, Davit
dc.contributor.authorTanaka, Midori
dc.contributor.authorPedersen, Marius
dc.contributor.authorHardeberg, Jon Yngve
dc.date.accessioned2021-01-14T09:23:00Z
dc.date.available2021-01-14T09:23:00Z
dc.date.created2020-09-30T18:36:55Z
dc.date.issued2020
dc.identifier.citationCEUR Workshop Proceedings. 2020, 2688 .en_US
dc.identifier.issn1613-0073
dc.identifier.urihttps://hdl.handle.net/11250/2722936
dc.description.abstractWe interpret our surrounding based on the visual stimuli, and perceive objects and materials around us to have various attributes, like color, glossiness, and translucency. We analyze the three-dimensional world based on the two-dimensional images detected by our retina. The state-of-the-art works conclude that the human visual system has a poor ability to fully understand and invert the complex optical nature of light and matter interaction. Some authors rather propose that the human brain calculates image statistics to perceive appearance, demonstrating correlation between perceptual attributes and various statistical metrics. However, the illustrated examples are usually unrealistic nearly-perfect stimuli, making real-life robustness of the findings questionable. In this study, we analyzed image statistics of photos of real world objects, and assessed the performance of statistical image metrics proposedly used by the human visual system. We identified very interesting trends, as well as limitations.en_US
dc.language.isoengen_US
dc.publisherCEUR Workshop Proceedingsen_US
dc.relation.urihttp://ceur-ws.org/Vol-2688/paper5.pdf
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleImage Statistics as Glossiness and Translucency Predictor in Photographs of Real-world Objectsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber15en_US
dc.source.volume2688en_US
dc.source.journalCEUR Workshop Proceedingsen_US
dc.identifier.cristin1835827
dc.relation.projectNorges forskningsråd: 288187en_US
dc.description.localcodeCopyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Colour and Visual Computing Symposium 2020, Gjøvik, Norway, September 16-17, 2020.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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