Image segmentation methods for quick characterization of ore chip using RGB images

Abstract

The ability to forecast geometallurgical properties during resource extraction is essential to optimize the mine to mill process. Models for mine planning thus often incorporate attributes related to processability. The analysis of these attributes in a laboratory can be time- and cost intensive. Only a limited number of data may be available. During production, grade control drilling may provide access to many more samples. Conducting laboratory analysis to each of these samples would be not realistic. If there was an opportunity to quickly obtain related proxy data, as physical characteristics that can stand in for direct measurements, then these indices could be estimated, certainly less precise but with a significantly increased spatial density. A moderately simple approach to acquire data from grade control drilling is to take digital Red, Green and Blue spectral bands images (RGB images) in from core trays. Although these capture only three spectral band regions, images can contain valuable texture and colour related information. A first necessary step is to automatically extract from an image and analyse objects, that represent ore particles or mineral content. This study aims to investigate the performance of different available segmentation methods under field conditions. First an overview of methods for image segmentation as a basis to create objects is presented. Objects can be related to single grains and minerals within the grains. The aim is to provide a basis for texture feature extraction related to granular rock, such as found in chip trains. Modern image analysis provides a large number of methods for segmentation and classification of objects. This work focuses on evaluating performance on images of 3 levels of complexity of pixel- based segmentation for complex or less noisy images and object-based segmentation (Watershed, Simple Linear Iterative Clustering and Quickshift) as a more advanced and universal method.