Leaves Shape Classification Using Curvature and Fractal Dimension

  • João B. Florindo
  • André R. Backes
  • Odemir M. Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

The great biodiversity of species makes the plants classification a very complex and time-consuming task. The leaf is an important characteristic of the plant and it is present independently of season or plant maturity. The most relevant information about the leaf relies on shape. Its study enables us to discriminate a large set of species and to speed up the measures extraction process, which is traditionally performed manually. This paper presents a novel approach to leaf shape identification based on curvature complexity analysis. By using the Curvature Scale Space (CSS), a curve describing the complexity of the shape is achieved. Descriptors computed from this curve are used to classify a set of leaves shapes. Results demonstrate the potential of the technique, which overcome traditional shape analysis methods found in literature.

Keywords

shape analysis curvature complexity fractal dimension 

References

  1. 1.
    Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31(9), 983–1001 (1998)CrossRefGoogle Scholar
  2. 2.
    da S. Torres, R., Falcão, A., da F. Costa, L.: A graph-based approach for multiscale shape analysis. Pattern Recognition 37(6), 1163–1174 (2003)Google Scholar
  3. 3.
    Wang, Z., Chi, Z., Feng, D.D.: Shape based leaf image retrieval. IEE Proceedings-Vision Image and Signal Processing 150(1), 34–43 (2003)CrossRefGoogle Scholar
  4. 4.
    Zhenjiang, M.: Zernike moment-based image shape analysis and its application. Pattern Recognition Letters 21(2), 169–177 (2000)CrossRefGoogle Scholar
  5. 5.
    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)MATHCrossRefGoogle Scholar
  6. 6.
    Manoel, E.T.M., da F. Costa, L., Streicher, J., Muller, G.B.: Multiscale fractal characterization of three-dimensional gene expression data. In: SIBGRAPI, pp. 269–274. IEEE Computer Society, Los Alamitos (2002)Google Scholar
  7. 7.
    da F. Costa, L., Jr., R.M.C.: Shape Analysis and Classification: Theory and Practice. CRC Press, Boca Raton (2000)Google Scholar
  8. 8.
    Backes, A.R., Bruno, O.M.: A new approach to estimate fractal dimension of texture images. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 136–143. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentic-Hall, New Jersey (2002)Google Scholar
  10. 10.
    Witkin, A.P.: Scale space filtering: a new approach to multi-scale descriptions. In: ICASSP - IEEE International Conference on Acoustics, Speech, and Signal Processing, GRETSI, pp. 79–95 (2003)Google Scholar
  11. 11.
    Mokhtarian, F., Abbasi, S.: Matching shapes with self-intersections: application to leaf classification. IEEE Transactions on Image Processing 13(5) (2004)Google Scholar
  12. 12.
    Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold (2001)Google Scholar
  13. 13.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)MATHGoogle Scholar
  14. 14.
    de Plotze, R.O., Falvo, M., Pádua, J.G., Bernacci, L.C., Vieira, M.L.C., Oliveira, G.C.X., Bruno, O.M.: Leaf shape analysis using the multiscale minkowski fractal dimension, a new morphometric method: a study with passiflora (passifloraceae). Canadian Journal of Botany 83(3), 287–301 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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