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.

References

  1. Achlioptas, D. (2001). Database-friendly random projections. In Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, PODS 7801, pages 274-281, New York, NY, USA. ACM.
  2. Amankwah, A. and Aldrich, C. (2011). Automatic ore image segmentation using mean shift and watershed transform. In Radioelektronika (RADIOELEKTRONIKA), 2011 21st International Conference, pages 1-4.
  3. Baraniuk, R., Davenport, M., DeVore, R., and Wakin, M. (2008). A simple proof of the restricted isometry property for random matrices. Constructive Approximation, 28(3):253-263.
  4. Beucher, S. and Lantuejoul, C. (1979). Use of watersheds in contour detection. In International Workshop on image processing, real-time edge and motion detection/estimation.
  5. Bingham, E. and Mannila, H. (2001). Random projection in dimensionality reduction: applications to image and text data. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 7801, pages 245-250, New York, NY, USA. ACM.
  6. Bull, G., Gao, J., and Antolovich, M. (2013). Image segmentation using random features. In The 2013 5th International Conference on Graphic and Image Processing (ICGIP 2013).
  7. Calderbank, R., Jafarpour, S., and Schapire, R. (2009). Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain. Manuscript.
  8. Candes, E. and Tao, T. (2005). Decoding by linear programming. Information Theory, IEEE Transactions on, 51(12):4203-4215.
  9. Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27. Software available at http://www.csie.ntu.edu.tw/ ~cjlin/libsvm.
  10. Comaniciu, D. and Meer, P. (1999). Mean shift analysis and applications. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1197-1203.
  11. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20:273-297.
  12. Demenegas, V. (2008). Fragmentation analysis of optimized blasting rounds in the aitik mine: effect of specific charge. Master's thesis, Lulea° tekniska universitet.
  13. Dollar, P., Tu, Z., Tao, H., and Belongie, S. (2007). Feature mining for image classification. In Computer Vision and Pattern Recognition, 2007. CVPR 7807. IEEE Conference on, pages 1-8.
  14. Donoho, D. (2006). Compressed sensing. Information Theory, IEEE Transactions on, 52(4):1289-1306.
  15. Gao, J., Shi, Q., and Caetano, T. S. (2012). Dimensionality reduction via compressive sensing. Pattern Recognition Letters, 33(9):1163 - 1170.
  16. Girdner, K., Kemeny, J., Srikant, A., and McGill, R. (1996). The split system for analyzing the size distribution of fragmented rock. In Franklin and Katsabanis, editors, Measurement of Blast Fragmentation - Proceedings of the FRAGBLAST 5 Workshop., pages 101-108, Balkema.
  17. Li, P., Hastie, T. J., and Church, K. W. (2006). Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 7806, pages 287-296, New York, NY, USA. ACM.
  18. Liu, L. and Fieguth, P. (2012). Texture classification from random features. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(3):574-586.
  19. Meyer, F. and Beucher, S. (1990). Morphological segmentation. Journal of Visual Communication and Image Representation, 1(1):21 - 46.
  20. Noy, M. J. (2013). Automated rock fragmentation measurement with close range digital photogrammetry. In Sanchidrian Blanco, J. A.and Singh, A. K., editor, Measurement and Analysis of Blast Fragmentation: Workshop FRAGBLAST 10 - The 10th International Symposium on Rock Fragmentation by Blasting., pages 13-21, Boca Raton, Fla. CRC Press/Balkema.
  21. Siddiqui, F., Shah, S. A., and Behan, M. (2009). Measurement of size distribution of blasted rock using digital image processing. Engineering Sciences, 20(2):81- 93.
  22. Thurley, M. (2009). Fragmentation size measurement using 3d surface imaging. In Blanco, J. A. S., editor, Fragblast 9 : Rock Fragmentation By Blasting. Proceedings of the 9th International Symposium On Rock Fragmentation By Blasting, pages 229-237, Boca Raton, Fla. CRC Press/Balkema.
  23. Thurley, M. (2013). Automated, on-line, calibration-free, particle size measurement using 3d profile data. In Sanchidrian Blanco, J. A.and Singh, A. K., editor, Measurement and Analysis of Blast Fragmentation: FRAGBLAST 10 - The 10th International Symposium on Rock Fragmentation by Blasting., pages 23-32, Boca Raton, Fla. CRC Press/Balkema.
  24. Thurley, M. J. and Ng, K. C. (2005). Identifying, visualizing, and comparing regions in irregularly spaced 3d surface data. Computer Vision and Image Understanding, 98(2):239-270.
  25. Viola, P. and Jones, M. (2002). Robust real-time object detection. International Journal of Computer Vision, 57(2):137-154.
  26. Wright, J., Ganesh, A., Rao, S., Peng, Y., and Ma, Y. (2009). Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I., and Culotta, A., editors, Advances in Neural Information Processing Systems 22, pages 2080-2088.
  27. Zhang, K., Zhang, L., and Yang, M.-H. (2012). Real-time compressive tracking. In Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C., editors, Computer Vision - ECCV 2012, volume 7574 of Lecture Notes in Computer Science, pages 864-877. Springer Berlin Heidelberg.
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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