Recognition of Two-Dimensional Shapes Based on Dependence Vectors

  • Krzysztof Gdawiec
  • Diana Domańska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)


The aim of this paper is to present a new method of two-dimensional shape recognition. The method is based on dependence vectors which are fractal features extracted from the partitioned iterated function system. The dependence vectors show the dependency between range blocks used in the fractal compression. The effectiveness of our method is shown on four test databases. The first database was created by the authors and the other ones are: MPEG7 CE-Shape-1PartB, Kimia-99, Kimia-216. Obtained results have shown that the proposed method is better than the other fractal recognition methods of two-dimensional shapes.


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  1. 1.
    Amit, Y.: 2D Object Detection and Recognition: Models, Algorithms, and Networks. MIT Press, Cambridge (2002)Google Scholar
  2. 2.
    Barnsley, M.: Fractals Everywhere. Academic Press, Boston (1988)MATHGoogle Scholar
  3. 3.
    Chandran, S., Kar, S.: Retrieving Faces by the PIFS Fractal Code. In: Proceedings 6th IEEE Workshop on Applications of Computer Vision, pp. 8–12 (December 2002)Google Scholar
  4. 4.
    Gdawiec, K.: Pseudofractal 2D Shape Recognition. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 403–410. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Gdawiec, K.: Shape Recognition Using Partitioned Iterated Function Systems. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 451–458. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Gdawiec, K., Domańska, D.: Partitioned Iterated Function Systems with Division and Fractal Dependence Graph in Recognition of 2D Shapes. International Journal of Applied Mathematics and Computer Science 21(4), 757–767 (2011)CrossRefGoogle Scholar
  7. 7.
    Fisher, Y.: Fractal Image Compression: Theory and Application. Springer, New York (1995)CrossRefGoogle Scholar
  8. 8.
    Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape Descriptors for Non-rigid Shapes with a Single Closed Contour. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 424–429 (June 2000)Google Scholar
  9. 9.
    Mozaffari, S., Faez, K., Faradji, F.: One Dimensional Fractal Coder for Online Signature Recognition. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 2, pp. 857–860 (August 2006)Google Scholar
  10. 10.
    Neil, G., Curtis, K.M.: Shape Recognition Using Fractal Geometry. Pattern Recognition 30(12), 1957–1969 (1997)CrossRefGoogle Scholar
  11. 11.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Their Shock Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krzysztof Gdawiec
    • 1
  • Diana Domańska
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaPoland

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