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Using Artificial Vision for the Microscopic Identification of Ores with Reflected Light: Preliminary Results.

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  • Engineering
  • Computing & Technology :: Geological
  • Petroleum & Mining Engineering [C08]
  • Ingénierie
  • Informatique & Technologie :: Géologie
  • Ingénierie Du Pétrole & Des Mines [C08]
  • Medicine


TextoSGA_terminado Using Artificial Vision for the Microscopic Identification of Ores with Reflected Light: Preliminary Results. Castroviejo Ricardo, Brea Carolina, Pérez-Barnuevo Laura Universidad Politécnica de Madrid, ETSI Minas, c/Ríos Rosas, 2, 28003_Madrid (Spain) Catalina J.Carlos, Segundo Fernando AITEMIN, Parque Tecnológico Leganés, 28919_Madrid (Spain) Bernhardt H.-Juergen Ruhr_Universitaet Bochum, Z. Elektronen-Mikrosonde, Universitaetsstr. 150, D_44801 Bochum (Germany) Pirard Eric Université de Liège, GeMMe, Sart Tilman B52, Liège 4000 (Belgium) Abstract. Traditional identification of ore minerals with reflected light microscopy relies heavily on the experience of the observer. Qualified observers have become a rarity, as ore microscopy is often neglected in today’s university training, but since it furnishes necessary and inexpensive information, innovative alternatives are needed, especially for quantification. Many of the diagnostic optical properties of ores defy quantification, but recent developments in electronics and optics allow new insights into the reflectance and colour properties of ores. Preliminary results for the development of an expert system aimed at the automatic identification of ores based on their reflectance properties are presented. The discriminatory capacity of the system is enhanced by near IR reflectance measures, while UV filters tested to date are unreliable. Interaction with image analysis software through a wholly automated microscope, to furnish quantitative and morphological information for geometallurgy, relies on automated identification of the ores based on the measured spectra. This methodology increases enormously the performance of the microscopist, nevertheless supervision by an expert is always needed. Keywords: Automated Ore Microscopy, computer vision, Multispectral reflectance data, VNIR, Geometallurgy. 1 Introduction Reflected light microscopy has been the

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