Abstract
Machine vision systems typically classify images of a flotation froth surface into one of a distinct set of classes. This process typically involves having an experienced operator identify a set of froth classes. After this, a machine vision system is trained to identify these froth classes. Identifying these froth classes is particularly challenging for froths which have “dynamic” bubble size distributions. Using unsupervised clustering algorithms, it is possible to automatically learn these froth classes without user input. Validation of this technique is done by showing that the identified froth classes have statistically different relationships between the froth velocity and concentrate grade.
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