Mineral Identification Using Color Spaces and Artificial Neural Networks

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dc.contributor.authorBaykan, Nurdan Akhan
dc.contributor.authorYılmaz, Nihat
dc.date.accessioned2020-03-26T18:04:41Z
dc.date.available2020-03-26T18:04:41Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIdentification of minerals and percentage of their area within a thin section of rock are important for identifying and naming rocks. Colors of minerals are the basic factors for identification. In this study, an artificial neural network is used for the classification of minerals. Optical data of thin sections is acquired from the rotating polarizing microscope stage. For the first analysis we selected a set of parameters based on red, green. blue (RGB) and the second based on hue, saturation, value (HSV) color spaces are extracted from the segmented minerals within each data set. A neural network with k-fold cross validation is trained with manually classified mineral samples based on their pixel values. The most successful artificial network to date is the three-layer feed forward network which uses minimum square error correction. The network uses 6 distinct input parameters to classify 5 different minerals, namely, quartz, muscovite, biotite, chlorite, and opaque. Testing the network with previously unseen mineral samples yielded successful results as high as 81-98%.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects CoordinatorshipSelcuk University; TUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors would like to thank Gursel Kansun for manually identifying the minerals used in this study. The authors are grateful to Selcuk University Scientific Research Projects Coordinatorship and TUBITAK (The Scientific and Technological Research Council of Turkey) for after press support of the manuscript. The authors would also like to thank E. Grunsky and the two reviewers of this manuscript for their helpful suggestions.en_US
dc.identifier.citationBaykan, N. A., Yılmaz, N., (2010). Mineral Identification Using Color Spaces and Artificial Neural Networks. Computers & Geosciences, 36(1), 91-97. doi: org/10.1016/j.cageo.2009.04.009.
dc.identifier.doi10.1016/j.cageo.2009.04.009en_US
dc.identifier.endpage97en_US
dc.identifier.issn0098-3004en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage91en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cageo.2009.04.009
dc.identifier.urihttps://hdl.handle.net/20.500.12395/25068
dc.identifier.volume36en_US
dc.identifier.wosWOS:000272419700010en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBaykan, Nurdan Akhan
dc.institutionauthorYılmaz, Nihat
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofComputers & Geosciencesen_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.titleMineral Identification Using Color Spaces and Artificial Neural Networksen_US
dc.typeArticleen_US

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