Raw ore selection by artificial vision

Some deposits of inhomogeneous materials, which occur near the surface and are exploitable by open-pit mining, may be characterized in real time prior to mining through ground surface imagery. If the slice to be stripped is n ot overly thick, surface texture and color properties may be extrapolated t o its entire thickness. Image-analysis techniques for processing ground-sur face images acquired in situ or in the laboratory shortly after their acqui sition have been developed. These images yield pattern vectors representati ve of red, green and blue (RGB) color component distributions and hue, satu ration and brightness (HSB) texture parameters. The techniques were applied to a sandy ore deposit containing three lithotypes. Geostatistical analysi s indicated that the data used to characterize the lithotypes were reliable . The correct recognition of the lithotypes was carried out using a multiba rycenter classification algorithm. Such in situ image-analysis procedures p rovide a means for selecting the ore to be mined, selecting the proper ore- processing method and determining the appropriate blend to be processed. Th is can be done either before or during mining.