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Illumination-Invariant Morphological Texture Classification

  • Allan Hanbury
  • Umasankar Kandaswamy
  • Donald A. Adjeroh
Conference paper
Part of the Computational Imaging and Vision book series (CIVI, volume 30)

Abstract

We investigate the use of the standard morphological texture characterisation methods, the granulometry and the variogram, in the task of texture classification. These methods are applied to both colour and greyscale texture images. We also introduce a method for minimising the effect of different illumination conditions and show that its use leads to improved classification. The classification experiments are performed on the publically available Outex 14 texture database. We show that using the illumination invariant variogram features leads to a significant improvement in classification performance compared to the best results reported for this database.

Keywords

Mathematical morphology texture variogram granulometry illumination invariance 

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References

  1. [1]
    V. Arvis, C. Debain, M. Berducat, and A. Benassi. Generalisation of the cooccurrence matrix for colour images: Application to colour texture classification. Image Analysis and Stereology, 23(1):63–72, 2004.Google Scholar
  2. [2]
    P. Brodatz. Textures: a photographic album for artists and designers. Dover, 1966.Google Scholar
  3. [3]
    D. Chetverikov. Fundamental structural features in the visual world. In Proceedings of the International Workshop on Fundamental Structural Properties in Image and Pattern Analysis, pages 47–58, 1999.Google Scholar
  4. [4]
    G. Finlayson, S. Chatterjee, and B. Funt. Color angular indexing. The Fourth European Conference on Computer Vision, European Vision Society, 11:16–25, 1996.Google Scholar
  5. [5]
    I. Foucherot, P. Gouton, J. C. Devaux, and F. Truchetet. New methods for analysing colour texture based on the Karhunen-Loeve transform and quantification. Pattern Recognition, 37:1661–1674, 2004.CrossRefGoogle Scholar
  6. [6]
    R. Gonzalez and R. Woods. Digital Image Processing. Peason Education, Inc, 2002.Google Scholar
  7. [7]
    A. Hanbury. Mathematical morphology applied to circular data. In P. Hawkes, editor, Advances in Imaging and Electron Physics, volume 128, pages 123–204. Academic Press, 2003.Google Scholar
  8. [8]
    S. D. Hordley, G. D. Finlayson, G. Schaefer, and G. Y. Tian. Illuminant and device invriant colour using histogram equalisation. Technical Report SYS-C02-16, University of East Anglia, 2002.Google Scholar
  9. [9]
    U. Kandaswamy, A. Hanbury, and D. Adjeroh. Illumination minvariant color texture descriptors. Manuscript in preparation.Google Scholar
  10. [10]
    D. Lafon and T. Ramananantoandro. Color images. Image Analysis and Stereology, 21(Suppl 1):S61–S74, 2002.Google Scholar
  11. [11]
    A. Mojsilović, J. Kova\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\smile}$}}{c} \)ević, D. Kall, R. J. Safranek, and S. K. Ganapathy. The vocabulary and grammar of color patterns. IEEE Trans. on Image Processing, 9(3):417–431, 2000.CrossRefGoogle Scholar
  12. [12]
    T. Mäenpää and M. Pietikäinen. Classification with color and texture: jointly or separately? Pattern Recognition, 37:1629–1640, 2004.CrossRefGoogle Scholar
  13. [13]
    T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen, and S. Huovinen. Outex — new framework for empirical evaluation of texture analysis algorithms. In Proceedings of the 16th ICPR, volume 1, pages 701–706, 2002.Google Scholar
  14. [14]
    T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(7):971–987, 2002.CrossRefGoogle Scholar
  15. [15]
    C. Palm. Color texture classification by integrative co-occurrence matrices. Pattern Recognition, 37:965–976, 2004.CrossRefGoogle Scholar
  16. [16]
    C. Palm and T. M. Lehmann. Classification of color textures by gabor filtering. Machine Graphics and Vision, 11(2/3):195–219, 2002.Google Scholar
  17. [17]
    A. R. Rao. A Taxonomy for Texture Description and Identification. Springer-Verlag, 1990.Google Scholar
  18. [18]
    A. R. Rao and G. L. Lohse. Identifying high level features of texture perception. CVGIP: Graphical Models and Image Processing, 55(3):218–233, 1993.CrossRefGoogle Scholar
  19. [19]
    J. Serra. Image Analysis and Mathematical Morphology. Academic Press, London, 1982.Google Scholar
  20. [20]
    P. Soille. Morphological Image Analysis. Springer, second edition, 2002.Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Allan Hanbury
    • 1
  • Umasankar Kandaswamy
    • 2
  • Donald A. Adjeroh
    • 2
  1. 1.PRIP group, Institute of Computer-Aided AutomationVienna University of TechnologyViennaAustria
  2. 2.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

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