Using visual texture analysis to classify raw coal components
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IEEE
Abstract
Coal ore isn’t a uniform material. In order to
optimize the coal liberation process it is necessary to classify
a coal ore sample into its constituent components as quickly
and cheaply possible. This paper investigates whether it is
feasible to employ image processing and pattern recognition to
segment a photographic image of coal ore into its various mineral
components prior to the sample being crushed. The key to solving
this classification problem is to model the visual texture of the
various coal components by means of a low-dimensional texture
space consisting of two main dimensions, namely: roughness
and regularity. The regularity of each texture is estimated by
means of a novel model-based approach. The distribution of the
various coal components in the resultant feature space is modelled
by means of a mixtures model and a simple nearest-neighbour
decision rule is used to classify each pixel in the image. The
performance of the classification system is encouraging and shows
the feasibility of our idea
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Citation
Van Vuuren, P.A. et al. 2015. Using visual texture analysis to classify raw coal components. 22nd International Conference on Systems, Signals and Image Processing. Proceedings of IWSSIP 2015, London, 10-12 Sep: 212-215. [https://doi.org/10.1109/IWSSIP.2015.7314214]