Image processing applications for customized mining and ore classification

During the mining operation, ore sorting and directing different grade ores to different processing circuits is a manual task in most of working mines, but this work puts a step forward toward automation of this process. The radical development in the area of image and data processing allows speedy processing of the full color digital images for the preferred investigations. In this paper, an approach has been proposed to classify the ores for blast furnace feed, based on the visual texture of the ore particles. The visual texture of ore particles vary with the mineral contents, for example, blue dust, hard ore, soft ore, etc. This information can be quantified by using image processing technique in red, green, and blue color space and first- and second-order statistical analysis. Commonly used Hartlics textural features were calculated along with red, green, and blue color values for 5?×?5-pixel size windowpanes extracted from five separate images. Results obtained show encouraging accuracy to apply the approach to develop an expert system for online ore quality monitoring to control the ore blending in the feed ore circuits as well as separating gangue minerals present in the feed ores. Matlab 6.5 was used for visual textural analysis and classification.