FLOTATION FROTH IMAGE ANALYSIS BY USE OF
THREE-DIMENSIONAL FEATURE EXTRACTION ALGORITHMS
Chris Aldrich1,2,*
1
Department of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines,
Curtin University, GPO Box U1987, 6248, WA, Australia
2
Department of Process Engineering, University of Stellenbosch,
Private Bag X1, Matieland, 7602, Stellenbosch, South Africa
*Chris.aldrich@curtin.edu.au
ABSTRACT
Computer vision plays an increasingly important part in the monitoring and control of mineral processing
systems. This includes ore characterization systems, as used in ore sorting, froth image analysis in
flotation, particle characterization on feed systems to reactors and metallurgical furnaces, etc. Image
analysis is often based on the extraction of features from images of the processing system, which are then
related to some key performance indicator, such as grade, recovery or processing state by use of a model.
Typically, these key performance indicators are hard to measure and as a result, the calibration or
inferential models are based on an abundance of image features that are inexpensively obtained online,
which have to be related to a comparatively small set of key performance indicator measurements.
Semisupervised learning is one approach to deal with this problem, but it is also possible to approach the
problem in a spatiotemporal framework, where features are extracted simultaneously from sets of images
acquired over a specific time window, which can be seen as a 3 rd dimension.
In this paper, the use of three-dimensional feature extraction methods is therefore explored, by comparing
three-dimensional local binary pattern and greyscale co-occurrence matrices with their two-dimensional
counterparts in froth flotation image analysis.
Figure 1 – General approach to textural image analysis for a froth flotation system. With the use of 3D
algorithms, the dynamic nature of the features is also accounted for, resulting in fewer, but potentially
more information rich features than with 2D approaches.
KEYWORDS
Froth flotation, Textural feature extraction, Local binary patterns, Grey level dependence matrix