Case study in effects of color spaces for mineral identification

Abstract

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