Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition

Olga Baklanova, Olga Ya Shvets

Abstract

It is described the algorithm for automatic segmentation of colour images of ores, using the methods of cluster analysis. There are some examples illustrated using of the algorithm in the solving of mineral rock recognition problems. Results of studies are demonstrated different colour spaces by k-means clustering. It was supposed the technique of pre- computing the values of the centroids. There is formulas translation metrics colour space HSV. The effectiveness of the proposed method lies in the automatic identification of interest objects on the total image, tuning parameters of the algorithm is a number that indicates the amount allocated to the segments. This paper contains short description of cluster analysis algorithm for the mineral rock recognition in the mining industry.

References

  1. Harvey, B., Tracy, R.J., 1995. Petrology: Igneous, Sedimentary, and Metamorphic, 2nd ed. New York: W.H. Freeman.
  2. Baklanova, O.E., 2013. Development of algorithms for image recognition needed to assess the quality of the mineral species in the mining industry. Abstracts of International Conference «Mathematical and Informational Technologies, MIT 2013»,VrnjackaBanja and Budva.
  3. Clarke, A. R., Eberhardt, C. N., 2002. Microscopy Techniques for Materials. Science Woodhead Publishing, CRC Press.
  4. Chris, P., 2002. Rocks and Minerals. Smithsonian Handbooks. New York: Dorling Kindersley, 2002.
  5. Farndon, J., 2006. The practical encyclopedia of rocks and minerals. How to Find, Identify, Collect and Maintain the World's best Specimens, with over 1000 Photographs and Artworks. London: Lorenz Books.
  6. Shaffer, P. R., Herbert, S. Z., Raymond P., 2001. Rocks, Gems and Minerals. Rev. ed. New York: St. Martin's Press.
  7. Isayenko, M. P., Borishanskaya, S. S., Afanasyev, E L., 1986. Keys to the main ore minerals in the reflected light. Moscow: Nedra.
  8. Tryon, R.C., 1939. Cluster analysis. London: Ann Arbor Edwards Bros.
  9. Panteleev, C., Egorova, O., Klykova, E., 2005. Computer microscopy. Moscow:Technosphere.
  10. Huang, Z., 1998. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 2:283-304.
  11. Odell, P. L., Duran, B. S., 1974. Cluster Analysis: A Survey, Springer-Verlag.
  12. Mandel, J.,1988. Cluster analysis. Moscow: Finance and statistics, 176 p.
  13. Ryzin, J. V., 1977. Classification and clustering. Proceeding of an advanced seminar: Academic press, Inc., New York, San Francisco, London, 390 p.
  14. Martin, D. R., Fowlkes, C.C., Malik, J., 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26, 2004, pp.530-549.
  15. Privalov, O. O., Butenko, L. N., 2007. Automatic segmentation of digital images medical-biological drugs method of cluster analysis. Modern science intensive technologies: nauch, 10, 2007, pp. 79-80.
  16. Tremeau, A., Borel, N. A., 1997. Region growing and Merging Algorithm to Color Segmentation. Pattern Recognition, PR(30), No. 7, July 1997. - pp. 1191- 1203.
  17. Gonsales, R. C., Woods, R. E., 2011. Digital image processing, 3rd edition, Pearson Education, 976 p.
  18. Baklanova, O. E., Uzdenbaev, Z.S., 2013. Development of methodology for analysis of mineral rocks in the mining industry. Joint issue of the Bulletin of the East Kazakhstan state technical University and Computer technology of Institute of computational technologies, Siberian branch of the Russian Academy of Sciences, Part 1, September, 2013. - P.60-66.
  19. Krasilnikov, N. N., 2011. Digital processing of 2D and 3D images. Saint-Petersburg, BHV-Petersburg, 608 p.
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Paper Citation


in Harvard Style

Baklanova O. and Shvets O. (2014). Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition . In Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014) ISBN 978-989-758-046-8, pages 165-171. DOI: 10.5220/0005022901650171


in Bibtex Style

@conference{sigmap14,
author={Olga Baklanova and Olga Ya Shvets},
title={Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition},
booktitle={Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)},
year={2014},
pages={165-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005022901650171},
isbn={978-989-758-046-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)
TI - Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition
SN - 978-989-758-046-8
AU - Baklanova O.
AU - Shvets O.
PY - 2014
SP - 165
EP - 171
DO - 10.5220/0005022901650171