Case Study in Effects of Color Spaces for Mineral Identification

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dc.contributor.authorBaykan, Nurdan Akhan
dc.contributor.authorYılmaz, Nihat
dc.contributor.authorKansun, Gürsel
dc.date.accessioned2020-03-26T17:47:21Z
dc.date.available2020-03-26T17:47:21Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractColor is the first parameter and one of the most powerful and important feature for mineral recognition via image processing. Although there are different color spaces, the most used of these are, three color spaces, namely RGB, HSV and CIELab were compared to find the best color space for the mineral identification in this study. These three color spaces are compared in terms of their suitability for identification. Using these three color space, an artificial neural network is used for the classification of minerals. Optical data of thin sections is acquired from the rotating polarizing microscope stage to classify 5 different minerals, namely, quartz, muscovite, biotite, chlorite, and opaque. The results show that RGB was efficient and suggested as the best color space for identification of minerals.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors are grateful to Selcuk University Scientific Research Projects Coordinatorship and TUBITAK (The Scientific and Technological Research Council of Turkey) for press support of the manuscript.en_US
dc.identifier.citationBaykan, N. A., Yılmaz, N., Kansun, G., (2010). Case Study in Effects of Color Spaces for Mineral Identification. Scientific Research and Essays, 5(11), 1243-1253.
dc.identifier.endpage1253en_US
dc.identifier.issn1992-2248en_US
dc.identifier.issue11en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1243en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24680
dc.identifier.volume5en_US
dc.identifier.wosWOS:000279559800004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBaykan, Nurdan Akhan
dc.institutionauthorYılmaz, Nihat
dc.institutionauthorKansun, Gürsel
dc.language.isoenen_US
dc.publisherAcademic Journalsen_US
dc.relation.ispartofScientific Research and Essaysen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networksen_US
dc.subjectMineralen_US
dc.subjectThin section imageen_US
dc.subjectRGBen_US
dc.subjectHSVen_US
dc.subjectCIELab
dc.titleCase Study in Effects of Color Spaces for Mineral Identificationen_US
dc.typeArticleen_US

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