Monitoring of mineral processing systems by using textural image analysis

• Published in: Minerals engineering Vol. 52; pp. 169 - 177
• Main Authors: Kistner, Melissa, Jemwa, Gorden T., Aldrich, Chris
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2013
• Subjects: Belt conveyors; Coal; Froth flotation; Mineral processing; Monitoring; Object recognition; Ore handling; Process control; Representations; Wavelet; Ore handling; Process control; Coal; Froth flotation
• ISSN: 0892-6875; 1872-9444
• DOI: 10.1016/j.mineng.2013.05.022

•Feature extraction from image data is important in mineral processing.•More informative features can be extracted by use of higher order methods.•Steerable pyramids yielded best results estimating grades from froth images.•Textons yielded best results estimating particle sizes from coal images. In the last few decades, developments in machine vision technology have led to innovative approaches to the control and monitoring of mineral processing systems. Image representation plays an important role in the performance of the recognition systems used in these approaches, where the use of feature representations based on second-order statistics of the image pixels have predominated. However, these representations may not adequately capture or express the visual textural structure associated with the observed patterns in images. In this study, the use of texton and complex multiscale wavelet representations (steerable pyramids) that exploit higher-order statistical regularities, is investigated. These techniques are applied to two image data sets: industrial platinum group metals froth flotation, and coal particles on a conveyor belt. Compared to grey level co-occurrence matrix and classical wavelet representations, these are observed to improve performance when used as input in the pattern recognition phase.