A Graph-Based Approach for Shape Skeleton Analysis

  • André R. Backes
  • Odemir M. Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

This paper presents a novel methodology to shape characterization, where a shape skeleton is modeled as a dynamic graph, and degree measurements are computed to compose a set of shape descriptors. The proposed approach is evaluated in a classification experiment which considers a generic set of shapes. A comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Multi-scale Fractal Dimension, is also performed. Results show that the method is efficient for shape characterization tasks, in spite of the reduced amount of information present in the shape skeleton.

Keywords

graph Shape Analysis Skeleton 

References

  1. 1.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Forms, pp. 362–380. MIT Press, Amsterdam (1967)Google Scholar
  2. 2.
    Blum, H., Nagel, R.: Shape description using weighted symmetric axis features. Pattern Recognition 10(3), 167–180 (1978)CrossRefMATHGoogle Scholar
  3. 3.
    da S. Torres, R., Falcão, A.X., da F. Costa, L.: A graph-based approach for multiscale shape analysis. Pattern Recognition 37, 1163–1174 (2003)CrossRefGoogle Scholar
  4. 4.
    Sebastian, T.B., Kimia, B.B.: Curves vs. skeletons in object recognition. Signal Processing 85(2), 247–263 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Wang, C., Cannon, D.J., Kumara, S.R.T., Guowen, L.: A skeleton and neural network-based approach for identifying cosmetic surface flaws. IEEE transactions on neural networks 6(5), 1201–1211 (1995)CrossRefGoogle Scholar
  6. 6.
    Zhu, X.: Shape recognition based on skeleton and support vector machines. In: Third International Conference on Intelligent Computing, pp. 1035–1043 (2007)Google Scholar
  7. 7.
    Backes, A.R., Casanova, D., Bruno, O.M.: A complex network-based approach for boundary shape analysis. Pattern Recognition 42(1), 54–67 (2009)CrossRefMATHGoogle Scholar
  8. 8.
    Wu, W.Y., Wang, M.J.: On Detecting the dominant points by the curvature-based polygonal approximation. CVGIP: Graphical Models Image Process 55, 79–88 (1993)Google Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentic-Hall, New Jersey (2002)Google Scholar
  10. 10.
    Plotze, R.O., Padua, J.G., Falvo, M., Vieira, M.L.C., Oliveira, G.C.X., Bruno, O.M.: Leaf shape analysis by the multiscale minkowski fractal dimension, a new morphometric method: a study in passiflora l (passifloraceae) 83, 287–301 (2005)Google Scholar
  11. 11.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    da F Costa, L., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: A survey of measurements. Advances in Physics 56(1), 167–242 (2007)CrossRefGoogle Scholar
  13. 13.
    Wuchty, S., Stadler, P.F.: Centers of complex networks. Journal of Theoretical Biology 223, 45–53 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)CrossRefGoogle Scholar
  15. 15.
    Sharvit, D., Chan, J., Tek, H., Kimia, B.B.: Symmetry-based indexing of image databases. Journal of Visual Communication and Image Representation 9(4), 366–380 (1998)CrossRefGoogle Scholar
  16. 16.
    Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold (2001)Google Scholar
  17. 17.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)MATHGoogle Scholar
  18. 18.
    Osowski, S., Nghia, D.D.: Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study. Pattern Recognition 35(9), 1949–1957 (2002)CrossRefMATHGoogle Scholar
  19. 19.
    Zhenjiang, M.: Zernike moment-based image shape analysis and its application”. Pattern Recognition Letters 21(2), 169–177 (2000)CrossRefGoogle Scholar
  20. 20.
    Bai, X., Latecki, L.J.: Path similarity skeleton graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(7), 1282–1292 (2008)CrossRefGoogle Scholar
  21. 21.
    Choi, W.P., Lam, K.M., Siu, W.C.: Extraction of the euclidean skeleton based on a connectivity criterion. Pattern Recognition 36(3), 721–729 (2003)CrossRefMATHGoogle Scholar
  22. 22.
    Bai, X., Latecki, L.J., Liu, W.Y.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. Pattern Analysis and Machine Intelligence 29(3), 449–462 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • André R. Backes
    • 1
  • Odemir M. Bruno
    • 2
  1. 1.Instituto de Ciências Matemáticas e de Computação (ICMC)Universidade de São Paulo (USP)São CarlosBrazil
  2. 2.Instituto de Física de São Carlos (IFSC)Universidade de São Paulo (USP)São CarlosBrazil

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