Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2755

Full Length Research Paper

Case study in effects of color spaces for mineral identification

Nurdan Akhan Baykan1*, Nihat Yılmaz2 and Gürsel Kansun3
1Department of Computer Engineering, University of Selcuk, Konya, Turkey. 2Department of Electric-Electronics Engineering, University of Selcuk, Konya, Turkey. 3Department of Geological Engineering, University of Selcuk, Konya, Turkey.
Email: [email protected]

  •  Accepted: 19 May 2010
  •  Published: 04 June 2010


Color 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.


Key words: Artificial neural networks, mineral, thin section image, RGB, HSV, CIELab