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Monitoring of rock fragmentation is a commercially important problem for the mining industry. Existing analysis methods either resort to physically sieving rock samples, or using image analysis software. The currently available software systems for this problem typically work with 2D images and often require a significant amount of time by skilled human operators, particularly to accurately delineate rock fragments. Recent research into 3D image processing promises to overcome many of the issues with analysis of 2D images of rock fragments. However, for many mines it is not feasible to replace their existing image collection systems and there is still a need to improve on methods used for analysing 2D images. This paper proposes a method for delineation of rock fragments using compressed Haar-like features extracted from small image patches, with classification by a support vector machine. The optimum size of image patches and the numbers of compressed features have been determined empirically. Delineation results for images of rocks were superior to those obtained using the watershed algorithm with manually assigned markers. Using compressed features is demonstrated to improve the computational efficiently such that a machine learning solution is viable.

Delineation of Rock Fragments by Classification of Image Patches using Compressed Random Features