Advertisement

Outline Matching of the 2D Shapes Using Extracting XML Data

  • Noreddine Gherabi
  • Mohamed Bahaj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

This paper presents an efficient shape matching method based on XML data, we extract the contour of the shape and this one is represented by set of points. Using corner detection method for representing the contour by a sequence of convex and concave segments. After, each segment is described by local and global features, this features are coded in string of symbols and stored in a XML file. Finally, using the dynamic programming, we find the optimal alignment between sequences of symbols. Results are presented and compared with existing methods using MATLAB for KIMIA-25 database and MPEG7 databases.

Keywords

XML DOM Shape descriptor Shape matching Dynamic Programming 

References

  1. 1.
    Wolfson, H.J.: On Curve Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 483–489 (1990)CrossRefGoogle Scholar
  2. 2.
    Kishon, E., Hastie, T., Wolfson, H.J.: 3-D Curve Matching using Splines. J. of Robotic Systems 8, 723–743 (1991)CrossRefGoogle Scholar
  3. 3.
    Barequet, G., Sharir, M.: Partial Surface Matching by Using Directed Footprints. In: Symposium on Computational Geometry, pp. C-9–C-10 (1996)Google Scholar
  4. 4.
    Biederman, I., Ju, G.: Surface versus edge-based determinants of visual recognitions. Cognit. Psychol. 20, 38–64 (1988)CrossRefGoogle Scholar
  5. 5.
    Geiger, D., Gupta, A., Costa, L.A., Vlontzos, J.: Dynamic Programming for Detecting, Tracking amd Matching Deformable contours. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(3), 294–302 (1995)CrossRefGoogle Scholar
  6. 6.
    Floreby, L.: A Multiscale Algorithm for Closed Contour Matching in Image Sequence. In: IEEE Intern. Conf. on Pattern Recognition, pp. 884–888 (1996)Google Scholar
  7. 7.
    Petrakis, E.G.M., Diplaros, A., Milios, E.: Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1501–1516 (2002)CrossRefGoogle Scholar
  8. 8.
    Yu, M.H., Lim, C.C., Jin, J.S.: Shape similarity using XML and portal technology. In: Visual Information Processing, VIP, Sydney, Australia (2006)Google Scholar
  9. 9.
    Fung, G.S.K., Yung, N.H.C., Pang, G.K.H.: Vehicle shape approximation from motion for visual traffic surveillance. In: Proc. IEEE 4th Int. Conf. on Intelligent Transportation Systems, pp. 201–206 (2001)Google Scholar
  10. 10.
    Manku, G.S., Jain, P., Aggarwal, A., Kumar, A., Banerjee, L.: Object tracking using affine structure for point correspondence. In: Proc. 1997 IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition, pp. 704–709 (1997)Google Scholar
  11. 11.
    Serra, B., Berthod, M.: 3-D model localization using highresolution reconstruction of monocular image sequences. IEEE Trans. Image Process. 6(1), 175–188 (1997)CrossRefGoogle Scholar
  12. 12.
    He, X.C., Yung, N.H.C.: Corner detector based on global and local curvature properties. Optical Engineering 47(5), 057008-1–057008-12 (2008)Google Scholar
  13. 13.
    Gherabi, N., Bahaj, M.: A new shape descriptor using XML. IJCSE 3, 1369–1376 (2011)Google Scholar
  14. 14.
    Ristad, E.S., Yianilos, P.N.: Learning string edit distance. IEEE Trans. Pattern Anal. Mach. Intell. 20(5), 522–532 (1998)CrossRefGoogle Scholar
  15. 15.
    Daliri, M.R., Delponte, E., Verri, A., Torre, V.: Shape Categorization Using String Kernels. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 297–305. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Super, B.J.: Learning chance probability functions for shape retrieval or classification. In: Proceedings of the IEEE Workshop on Learning in Computer Vision and Pattern Recognition (June 2004)Google Scholar
  17. 17.
    Attalla, E., Siy, P.: Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recognition 38(12) (2005)Google Scholar
  18. 18.
    Super, B.J.: Retrieval from shape databases using chance probability functions and fixed correspondence. Int. J. Pattern Recognition Artif. Intell. 20(8), 1117–1137 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Noreddine Gherabi
    • 1
  • Mohamed Bahaj
    • 1
  1. 1.FSTS, Department of Mathematics and Computer ScienceHassan 1st UniversityMorocco

Personalised recommendations