Abstract
This paper describes development of methods and algorithms of image recognition for mineral rocks. Algorithms of the cluster and morphological analysis to determinate colors and shapes for composition of rocks are described. This approach is actual because of existence of objects with similar color-brightness characteristics, but different shapes or objects that have similar color-brightness characteristics. Preliminary determination of group membership allows reducing the computational complexity of classification. Sorting by group produces color of the object is determined at the stage of segmentation. Few examples of segmentation algorithms in the solving of mineral rock recognition problems are described and discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harvey, B., Tracy, R.J.: Petrology: Igneous, Sedimentary, and Metamorphic, 2nd edn. W.H. Freeman, New York (1995)
Chris, P.: Rocks and minerals. In: Smithsonian Handbooks. Dorling Kindersley, New York (2002)
Clarke, A.R., Eberhardt, C.N.: Microscopy Techniques for Materials Science, 459 p. Woodhead Publishing, CRC Press, Cambridge (2002)
ISO 25706-83. Interstate standard. Magnifying glasses. Types, key parameters. General technical requirements. Date of Introduction 1984-01-01. [Electronic resource] (1984). http://docs.cntd.ru/document/gost-25706-83
Farndon, J.: 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. Lorenz Books, London (2006)
Mandel, I.D.: Cluster analysis. Finance and Statistics, Moscow, 176 p. (1988)
Baklanova, O.E., Shvets, O., Uzdenbaev, Z.: Automation system development for micrograph recognition for mineral ore composition evaluation in mining industry. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 436, pp. 604–613. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44654-6
Odell, P.L., Duran, B.S.: Cluster Analysis: A Survey. Springer, Heidelberg (1974)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discovery 2, 283–304 (1998)
Baklanova, O.E., Shvets, O.Y.: 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 (SIGMAP 2014), pp. 165–171. SCITERPRESS Science and Technology Publications, Lda (2014) doi:10.5220/0005022901650171
Gonsalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Education, Upper Saddle River (2011). p. 976
Baklanova, O.E., Shvets, O.Y.: Development of methods and algorithms of reduction for image recognition to assess the quality of the mineral species in the mining industry. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 75–83. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11331-9
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Baklanova, O.E., Baklanov, M.A. (2016). Methods and Algorithms of Image Recognition for Mineral Rocks in the Mining Industry. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-41009-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41008-1
Online ISBN: 978-3-319-41009-8
eBook Packages: Computer ScienceComputer Science (R0)