Skip to main content

Analysis of Meso Textures of Geomaterials Through Haralick Parameters

  • Conference paper
  • 1613 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

Abstract

The geomaterials used in this study are granites from Finland with very similar mineral composition. Visual evaluation of the rock texture is done to determine the most significant features of the patterns for the analysis of heterogeneity of meso textures are grain size and grain size spatial distribution. These are compared to results of parameters calculated using image structure analyser. Images are capture with a scanner of the polished slabs that are 9*9 cm in size. The geo textures are expressed by four main parameters: textural entropy, homogeneity, contrast and textural correlation. Reducing the number of parameters to entropy and textural correlation significantly reduce the calculation time. These two parameters are considered to be the most significant. The other two, homogeneity and contrast, can be estimated. The parameter textural correlation yields better results than does textural entropy. Comparison of the analysis of textures visually and using image analysis shows that textural parameters have to be further worked in order to have a better performance.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Marques, S.M.: Reconhecimento de padroes, methodos estatisticos e neuronais, Instituto Superior Tecnico (IST), IST press (1999)

    Google Scholar 

  2. Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  3. Taborda Duarte, M., Liu, H.Y., Lindqvist, P.-A., Kou, S.Q.: Miskovsky K.: Statistical modelling of the microstructure. accepted in Journal of Materials Engineering and Performance (in press)

    Google Scholar 

  4. Liu, H.Y., Roquete, M., Kou, S.Q., Lindqvist, P.A.: Characterization of rock heterogeneity and its numerical verification. Engineering Geology 72, 89–119 (2004)

    Article  Google Scholar 

  5. Duarte, M.T., Kou, S.Q., Lindqvist, P.-A., Miskovsky, K.: Mechanical heterogeneity of granites based on the weakest link theory (to be submitted)

    Google Scholar 

  6. Miskovsky, K., Taborda Duarte, M., Kou, S.Q., Lindqvist, P.-A.: Influence of the mineralogical composition and textural properties on the quality of course aggregates. Journal of Materials Engineering and Performance 13(2), 144–150 (2004)

    Article  Google Scholar 

  7. Lock, P.A., Jing, X.D., Zimmerman, R.W., Schlueter, E.M.: Predicting the permeability of sandstone from image analysis of pore structure. J. Appl. Phys. 10, 6311–6319 (2002)

    Article  Google Scholar 

  8. Fernlund, J.: The effect of particle form on sieve analysis: a test by image analysis. Eng. Geol. 50, 111–124 (1998)

    Article  Google Scholar 

  9. Image Structure Analyzer (ISA), Center for Biofilm Engineering’s, Montana State University, USA

    Google Scholar 

  10. Yang, X., Beyenal, H., Gary, G., Lewandowski, Z.: Quantifying biofilm structure using image analysis. Journal of Microbiological Methods 39, 109–119 (2000)

    Article  Google Scholar 

  11. Autio, J., Rantanen, L., Visa, A., Lukkarinen, S.: The classification of rock texture analyses by co-occurrence matrices and the Hough transform. In: Proc. of Geovision, International Symposium on Imaging Applications in Geology, Liege, Belgium, May 6-7 (1999)

    Google Scholar 

  12. Parti, M., Cramariuc, B., Gabbouj, M., Visa, A.: Rock texture retrieval using gray level co-occurrence matrix. In: Norsig, 5th Nordic Signal Processing Symposium, Tromsø (2002)

    Google Scholar 

  13. Williams, A.T., Wiltshire, R.J., Thomas, M.C.: Sand grain analysis- Image Processing, textural algorithms and neural nets. Computers & geosciences 24(2), 111–1 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Taborda Duarte, M., Robison Fernlund, J.M. (2005). Analysis of Meso Textures of Geomaterials Through Haralick Parameters. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_87

Download citation

  • DOI: https://doi.org/10.1007/11492542_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics