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Minerals Engineering, Volume 52, 2013, Pages 169-177.
Melissa Kistner, Gorden T. Jemwa, Chris Aldrich.
Department of Process Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602 Stellenbosch, South Africa and
Department of Metallurgical and Minerals Engineering, Western Australian School of Mines, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia.
Abstract
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.
