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Automatically Detecting Symmetries in Decorative Tiles

  • Rafael Dueire Lins
  • Daniel Marques Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

Symmetry information is used as the basis of a compression algorithm for images of decorative tiles yielding a compact representation. This allows faster network transmission and less space for storage of tile images. This paper presents an algorithm capable of automatically detecting the patterns of symmetry of images of tiles. The methodology developed may apply to any sort of repetitive symmetrical colour images and drawings.

Keywords

Image compression web pages ceramic tiles 

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References

  1. 1.
    Alt, H., et al.: Congruence, similarity and symmetries of geometric objects. ACM J.Comp. 4, 308–315 (1987)Google Scholar
  2. 2.
    Atallah, M.: On symmetry detection. IEEE Transactions on Computers 34(7), 663–666 (1985)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Avnir, D., Meyer, A.Y.: Quantifying the degree of molecular shape deformation: a chirality measure. J. Molecular Structure (Theochem) 94, 211–222 (1991)CrossRefGoogle Scholar
  4. 4.
    S.Cavalcanti and A.Menezes e Cruz. Tiles in the Secular Architecture of Pernambuco, 19th Century, Metalivros, 2002. Google Scholar
  5. 5.
    Berger, M.: Computer Graphics with Pascal. Addison-Wesley, Reading (1986)Google Scholar
  6. 6.
    Burton, F.W., Kollins, J.G., Alexandridis, N.A.: An implementation of the exponential pyramid data structure with applications etc. Computer Vision, Graphics, and I.Processing 25, 218–225 (1984)CrossRefGoogle Scholar
  7. 7.
    Eades, P.: Symmetry finding algorithms, Computational Morphology, pp. 41–51. Elsevier, Amsterdam (1988)Google Scholar
  8. 8.
    Gilat, G.: Chiral coefficient – a measure of the amount of structural chirality. J. Ph. A 22, 545 (1989)CrossRefGoogle Scholar
  9. 9.
    Grunbaum, B.: Measures of symmetry for convex sets. In: Symp. P.Math: A.Math.Soc., vol. 7, pp. 233–270 (1963)Google Scholar
  10. 10.
    Hel_Or, Y., Peleg, S., Avnir, D.: Chararcterization of right handed and left handed shapes. Computer Vision, Graphics, and Image Processing 53(2) (1991)Google Scholar
  11. 11.
    Highnam, P.T.: Optimal algorithms for finding the symmetries etc. Inf. Proc. Lett. 22, 219–222 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Krahe, J.L.: Detection of symmetric and radial structures in images. In: International Conference on Pattern Recognition, pp. 947–950 (1986)Google Scholar
  13. 13.
    Kuehnle, J.L.: Symmetry-based recognition of vehicle rears. Patt. Recognition Letters 12, 249–258 (1991)CrossRefGoogle Scholar
  14. 14.
    Levitt, T.S.: Domain independent object description and decomposition. In: Proceedings American Association of Artificial Intelligence, pp. 207–211 (1984)Google Scholar
  15. 15.
    Lins, R.D., Machado, D.S.A.: A Comparative Study of File Formats for Image Storage and Transmission. Journal of Electronic Imaging 13(1), 175–183 (2004)CrossRefGoogle Scholar
  16. 16.
    Lins, R.D.: A New File Format for Decorative Tiles. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 175–182. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Manmatha, R., Sawhney, H.S.: Finding Symmetry in Intensity Images, IBM Tech. Rep., Almaden (1995)Google Scholar
  18. 18.
    Miano, J.: Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. Addison Wesley, Reading (1999)Google Scholar
  19. 19.
    Murray, J.D., James, D., van Ryper, W.: Encyclopedia of Graphics File Formats. O’Reilly, Sebastopol (1996)Google Scholar
  20. 20.
    Nevatia, R., Binford, T.O.: Description and recognition complex curved obj. Art.Intell 8, 77–98 (1977)zbMATHCrossRefGoogle Scholar
  21. 21.
    Ogawa, H.: Symmetry analysis of line drawings etc. Patt. Recog. Letters 12, 9–12 (1991)CrossRefGoogle Scholar
  22. 22.
    Parui, S.K., Majumder, D.D.: Symmetry analysis by computer. Patt. Recog. 16, 63–67 (1983)CrossRefGoogle Scholar
  23. 23.
    Ponce, J.: On characterising ribbons and finding skewed symmetries. Computer Vision Graphics Image Processing 52, 328–340 (1990)CrossRefGoogle Scholar
  24. 24.
    Posch, S.: Detecting skewed symmetries. In: Inter. Conf. Patt. Recog., pp. 602–606 (August 2002)Google Scholar
  25. 25.
    Vasilier, A.A.: Recognition of symmetrical patterns in images. In: Int. Conf. Patt. Recog., pp. 1138–1140 (1984)Google Scholar
  26. 26.
    Wallace, G.K.: The JPEG Still Picture Compression Standard. CACM (34), 31–44 (1991)Google Scholar
  27. 27.
    Wolter, J., Woo, T., Volz, R.: Optimal algorithms for symmetry detection in two and three dimensions. The Visual Computer 1, 37–48 (1985)zbMATHCrossRefGoogle Scholar
  28. 28.
    Hamilton, E.: JPEG File Interchance Format. V 1.02, C-Cube Microsystems (September 1992)Google Scholar
  29. 29.
    Zabrodsky, H., Peleg, S., Avnir, D.: A measure of symmetry based on shape similarity. In: IEEE Conference on Computer Vision and Pattern Recognition, June 1992, pp. 703–706 (1992)Google Scholar
  30. 30.
    Zabrodsky, H., Peleg, S., Avnir, D.: Completion of occluded shapes using symmetry. In: IEEE Comp. Vision Patt. Recog., pp. 678–679 (1993)Google Scholar
  31. 31.
    Zabrodsky, H.: Computational Aspects of Pattern Characterization – Continous Symmetry, PhD Thesis, Hebrew University of Jerusalem (June 1993)Google Scholar
  32. 32.
    Zielke, T., Brauckmann, M., von Seelen, W.: Intensity and edge-based symmetry detection applied to car-following. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 865–873. Springer, Heidelberg (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rafael Dueire Lins
    • 1
  • Daniel Marques Oliveira
    • 1
  1. 1.Universidade Federal de PernambucoRecifeBrazil

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