Ore grade estimation by feature selection and voting using boundary detection in digital image analysis


In mining, rock classification plays a crucial role at different stages of the extraction process ranging from the design of the mine to mineral grading and plant control. In this paper we present a new method to improve rock classification using digital image analysis, feature selection based on mutual information and a voting process to take into account boundary information. We extract rock color and texture features and using mutual information we selected 14 from 36 features to represent the data in a lower dimensional space. The original image was divided into sub-images that are assigned to one class based on the selected color and texture features using a set of classifiers in cascade. Additionally, using rock boundary information, a voting process for the sub-images within the same blob is performed. We compare our results based on sub-image classification to those obtained after the voting process and to those previously published on the same rock image database. We show that the RMSE on rock composition classification on a test database decreased 8.8% by using our proposed voting method with the automatic segmentation with respect to direct sub-image classification. The RMSE decreased 29.5% relative to previously published results with the same database using a mixture of dry and wet rock images. The RMSE decreased even more if we considered separately dry and wet rocks. Our proposed method could be implemented in real-time to estimate mineral composition and can be used for online ore sorting and/or classification. © 2011 Elsevier B.V. All rights reserved.

International Journal of Mineral Processing
Leo Medina
Leo Medina
Assistant Professor

Leo teaches computer engineering courses at Usach, and his research interests are in the neural engineering and computational neuroscience fields. His work has contributed to understand how nerve fibers respond to electrical stimulation.