Advertisement

Comparison between 2D and 3D Local Binary Pattern Methods for Characterisation of Three-Dimensional Textures

  • Ludovic Paulhac
  • Pascal Makris
  • Jean-Yves Ramel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

Our purpose is to extend the Local Binary Pattern method to three dimensions and compare it with the two-dimensional model for three-dimensional texture analysis. To compare these two methods, we made classification experiments using three databases of three-dimensional texture images having different properties. The first database is a set of three-dimensional images without any distorsion or transformation, the second contains additional gaussian noise. The last one contains similar textures as the first one but with random rotations according x, y and z axis. For each of these databases, the three-dimensional Local Binary Pattern method outperforms the two-dimensional approach which has more difficulties to provide correct classifications.

Keywords

Solid texture Local Binary Pattern Method Classification experiments 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tuceryan, M., Jain, A.K.: 2.1. Texture Analysis. The Handbook of Patern Recognition and Computer Vision, 207–248 (1998)Google Scholar
  2. 2.
    Haralick, R.M.: Statistical and structural approaches to textures. Proceedings of the IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  3. 3.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  4. 4.
    Wang, L., He, D.C.: Texture classification using texture spectrum. Pattern Recognition 23(8), 905–910 (1990)CrossRefGoogle Scholar
  5. 5.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  6. 6.
    Tuceryan, M., Jain, A.K.: Texture segmentation using voronoi polygons. IEEE Transactions On Pattern Analysis And Machine Intelligence 12, 211–216 (1990)CrossRefGoogle Scholar
  7. 7.
    Chellappa, R., Jain, A.K.: Markov Random Fields Theory and Application. Academic Press, London (1993)Google Scholar
  8. 8.
    Mosquera, A., Cabello, D., Carreira, M., Penedo, M.: A fractal-based approach to texture segmentation. In: ICIPA 1992: Proceedings on the International Conference on Image Processing and its Application (1992)Google Scholar
  9. 9.
    Turner, M.: Texture discrimination by gabor functions. Biological Cybernetics 55, 71–82 (1986)Google Scholar
  10. 10.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE transaction on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)zbMATHCrossRefGoogle Scholar
  11. 11.
    Laine, A., Fan, J.: Textures classification by wavelet packets signatures. IEEE Transaction on Patern Analysis and Machine Intelligence 15, 1186–1191 (1993)CrossRefGoogle Scholar
  12. 12.
    Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4, 1549–1560 (1995)CrossRefGoogle Scholar
  13. 13.
    Stachowiak, G.P., Podsiadlo, P., Stachowiak, G.W.: A comparison of texture feature extraction methods for machine condition monitoring and failure analysis. Tribology Letters 20(2), 133–147 (2005)CrossRefGoogle Scholar
  14. 14.
    Suzuki, M.T., Yoshitomo, Y., Osawa, N., Sugimoto, Y.: Classification of solid textures using 3d mask patterns. In: ICSMC 2004: International Conference on Systems, Man and Cybernetics (2004)Google Scholar
  15. 15.
    Kovalev, V.A., Petrou, M., Bondar, Y.S.: Texture anisotropy in 3d images. IEEE Transaction on Image Processing 8, 346–360 (1999)CrossRefGoogle Scholar
  16. 16.
    Ghoneim, D.M., Toussaint, G., Constans, J.M., de Certaines, J.D.: Three dimensional texture analysis in mri: a preliminary evaluation in gliomas. Magnetic Resonance Imaging 21, 983–987 (2003)CrossRefGoogle Scholar
  17. 17.
    Showalter, C., Clymer, B.D., Richmond, B., Powell, K.: Three-dimensional texture analysis of cancellous bone cores evaluated at clinical ct resolutions. Osteoporos Int. 17, 259–266 (2006)CrossRefGoogle Scholar
  18. 18.
    Chen, X., Murphy, R.F.: Robust classification of subcellular location patterns in high resolution 3d fluorescence microscope images. In: EMBS 2004: Proceedings of the 26th Annual International Conference of the IEEE EMBS, pp. 1–5 (2004)Google Scholar
  19. 19.
    Jafari-Khouzani, K., Soltanian-Zadeh, H., Elisevich, K., Patel, S.: Comparison of 2d and 3d wavelet features for tle lateralization. In: Proceedings of the SPIE, vol. 5369 (2004)Google Scholar
  20. 20.
    Zhan, Y., Shen, D.: Deformable segmentation of 3d ultrasound prostate image using statistical texture matching method. IEEE transaction on medical imaging 25(3), 256–272 (2006)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence 29(6), 915–928 (2007)CrossRefGoogle Scholar
  22. 22.
    Ojala, T., Pietikäinen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE transaction on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  23. 23.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  24. 24.
    Kopf, J., Fu, C.W., Cohen-Or, D., Deussen, O., Lischinski, D., Wong, T.T.: Solid texture synthesis from 2d exemplars. In: SIGGRAPH 2007: Proceedings of the 34th International Conference and Exhibition on Computer Graphics and Interactive Techniques (2007)Google Scholar
  25. 25.
    Gool, L.J.V., Dewaele, P., Oosterlinck, A.: Texture analysis anno 1983. Computer Vision, Graphics, and Image Processing 29(3), 336–357 (1985)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ludovic Paulhac
    • 1
  • Pascal Makris
    • 1
  • Jean-Yves Ramel
    • 1
  1. 1.Laboratoire Informatique del’Université François Rabelais de Tours 

Personalised recommendations