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

Geoff Bull, Junbin Gao, Michael Antolovich

2014

Abstract

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.

Paper Citation

in Harvard Style

Bull G., Gao J. and Antolovich M. (2014). Delineation of Rock Fragments by Classification of Image Patches using Compressed Random Features . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 394-401. DOI: 10.5220/0004743203940401

in Bibtex Style

@conference{visapp14,
author={Geoff Bull and Junbin Gao and Michael Antolovich},
title={Delineation of Rock Fragments by Classification of Image Patches using Compressed Random Features},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={394-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004743203940401},
isbn={978-989-758-003-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Delineation of Rock Fragments by Classification of Image Patches using Compressed Random Features
SN - 978-989-758-003-1
AU - Bull G.
AU - Gao J.
AU - Antolovich M.
PY - 2014
SP - 394
EP - 401
DO - 10.5220/0004743203940401