Aplicación del análisis multi-espectral para el reconocimiento automatizado de menas metálicas

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, in...

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Detalles Bibliográficos
Autores: Catalina, Juan Carlos, Segundo, Fernando, Brea, Carolina, Pérez Barnuevo, Laura, Samper, Josefina, Espí, José Antonio, Sánchez, Lázaro, Castroviejo, Ricardo
Tipo de recurso: artículo
Fecha de publicación:2009
País:España
Institución:Universidad de Huelva (UHU)
Repositorio:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:español
OAI Identifier:oai:ariasmontano.uhu.es:10272/8007
Acceso en línea:http://hdl.handle.net/10272/8007
Access Level:acceso abierto
Palabra clave:Automated ore microscopy
Computer vision
Multispectral reflectance data
VNIR
Geometallurgy
Descripción
Sumario: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