Automated microscopic characterization of metallic ores with image analysis: a key to improve ore processing. I: test of the methodology

Ore microscopy has traditionally been an important support to control ore processing, but the volume of present day processes is beyond the reach of human operators. Automation is therefore compulsory, but its development through digital image analysis, DIA, is limited by various problems, such as the similarity in reflectance values of some important ores, their anisotropism, and the performance of instruments and methods. The results presented show that automated identification and quantification by DIA are possible through multiband (RGB) determinations with a research 3CCD video camera on a reflected light microscope. These results were obtained by systematic measurement of selected ores accounting for most of the industrial applications. Polarized light is avoided, so the effects of anisotropism can be neglected. Quality control at various stages and statistical analysis are important, as is the application of complementary criteria (e.g., metallogenetic). The sequential methodology is described and shown through practical examples.