Skip to main content
SpringerLink
Log in

Shape Classification Using B-spline Approximation and Contour Based Shape Descriptors

  • Conference paper
  • First Online:
Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017) (SoCPaR 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 737))

Included in the following conference series:

Abstract

In this paper we propose to compare some of contour based shape descriptors like Fourier descriptors, Radial function and Fourier of Radial function after applying a global B-spline approximation to the contours. Applying global B-spline approximation we can smooth and reduce the number of points in the contours. We used this technique for classification of fossil species and especially Brachiopods. The fossils classification has a great importance in palaeontological studies. On the one hand, they make it possible to understand the biodiversity in its morphological dimension. On the other hand, they show the morphological transformations suffered during the biological evolution. We compared the descriptors using City block distance and the recognition rate to measure the discrimination of the descriptors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Khouya, Y.A., Ghorbel, F., Alaa, N.: Brachiopods classification based on fusion of global and local complete and stable descriptors. In: 12th ACS/IEEE International Conference on Computer Systems and Applications, Marrakech, Morocco, 17–20 November 2015

    Google Scholar 

  2. Bachnou, A.: Modélisation des contours fermés: an-morph outil mis au point pour maitriser les composantes des profils latraux des ostracodes. perspective d’application systmatique. Géobis 32(5), 733–742 (1999)

    Google Scholar 

  3. Bailey, R., Srinath, M.: Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 18(4), 389–399 (1996)

    Article  Google Scholar 

  4. Belkasim, S., Shridar, M., Ahmadi, M.: Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1117–1138 (1991)

    Google Scholar 

  5. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  6. Crampton, J.: Elliptic Fourier shape analysis of fossil bivalves: some practical considerations. Lethaia 28(2), 179–186 (1995)

    Article  Google Scholar 

  7. Crimmins, T.R.: A complete set of Fourier descriptors for two dimensional shapes. IEEE Trans. Syst. Man Cybern. 12, 848–855 (1982)

    Google Scholar 

  8. Dalitz, C., Brandt, C., Goebbels, S., Kolanus, D.: Fourier descriptors for broken shapes. EURASIP J. Adv. Sig. Process. 2013, 161 (2013)

    Article  Google Scholar 

  9. El Hariri, K., Bachnou, A.: Describing ammonite shape using Fourier analysis. J. Afr. Earth Sci. 39, 347–352 (2004)

    Article  Google Scholar 

  10. Foote, M.: Perimeter-based Fourier analysis: a new morphometric method applied to the trolobite cranidium. J. Paleontol. 63(6), 880–885 (1989)

    Article  Google Scholar 

  11. Cohen, F.S., Huang, Z., Yang, Z.: Invariant matching and identification of curves using B-splines curve representation. IEEE Trans. Image Process. 4(1), 1–10 (1995)

    Article  Google Scholar 

  12. Farin, G.E.: Curves and Surfaces for CAGD: A Practical Guide, 5th edn. Morgan Kaufmann, Burlington (2001)

    Google Scholar 

  13. Ghorbel, F.: Stability of invariant Fourier descriptors and its inference in the shape classification. In: 11th International Conference in Pattern Recognition, The Hague, The Netherlands, 30 August–3 September 1992

    Google Scholar 

  14. Ghorbel, F.: Invariants de formes et de mouvement Onze cas du 1D au 4D et de l’euclidien aux projectifs. Arts Pi, Tunis (2013)

    Google Scholar 

  15. Jehan-Besson, S., Barlaud, M., Aubert, G.: DREAM\(^2\)S: deformable regions driven by an Eulerian accurate minimization method for image and video segmentation. Int. J. Comput. Vis. 53(1), 45–70 (2003)

    Article  MATH  Google Scholar 

  16. Kauppinen, H., Seppnen, T., Pietikinen, M.: An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 201–207 (1995)

    Article  Google Scholar 

  17. Kpalma, K., Ronsin, J.: Multiscale contour description for pattern recognition. Pattern Recogn. Lett. 27(13), 1545–1559 (2006)

    Article  Google Scholar 

  18. Mokhtarian, F., Abbasi, S.: Shape similarity retrieval under affine transforms. Pattern Recogn. 10(2), 31–41 (2002)

    Article  MATH  Google Scholar 

  19. Mokhtarian, F., Mackworth, A.K.: Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 34–43 (1986)

    Article  Google Scholar 

  20. Mokhtarian, F., Mackworth, A.K.: A theory of multiscale, curvaturebased shap representation for planar curves. IEEE PAMI 14, 789–805 (1992)

    Article  Google Scholar 

  21. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  22. Nagabhushana, S.: Computer Vision and Image Processing. New Age International (P) Limited, Delhi (2005)

    Google Scholar 

  23. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(3), 223–247 (2002)

    Article  MATH  Google Scholar 

  24. Persoon, E., Fu, K.: Shape discrimination using Fourier descriptors. IEEE Trans. Syst. Man Cybern. 7(3), 170–179 (1977)

    Article  MathSciNet  Google Scholar 

  25. Persoon, E., Fu, K.: Shape discrimination using Fourier descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 8(3), 388–397 (1986)

    Article  Google Scholar 

  26. Rui, Y., She, A.: A modified Fourier descriptor for shape matching in mars. Image Databases Multimed. Search 8, 165–180 (1998)

    Article  Google Scholar 

  27. Tieng, Q.M., Boles, W.W.: Recognition of 2D object contours using the wavelet transform zero crossing representation. IEEE Trans. PAMI 19(8), 910–916 (1997)

    Article  Google Scholar 

  28. Urdiales, C., Bandera, A., Sandoval, F.: Non-parametric planar shape representation based on adaptive curvature functions. Pattern Recogn. 35, 43–53 (2002)

    Article  MATH  Google Scholar 

  29. Yang, H.S., Lee, S.U., Lee, K.M.: Recognition of 2D object contours using starting-point-independent wavelet coefficient matching. J. Vis. Commun. Image Represent. 9(2), 171–181 (1998)

    Article  MathSciNet  Google Scholar 

  30. Zahn, C., Roskies, R.: Fourier descriptors for plane closed curves. Trans. Comput. 21(3), 269–281 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang, D.S., Lu, G.: A comparative study of Fourier descriptors for shape representation and retrieval. In: Proceedings of 5th Asian Conference on Computer Vision (ACCV 2002), 22–25 January, pp. 646–651, Melbourne, Australia (2002)

    Google Scholar 

  32. Zhang, D., Lu, G.: Study and evaluation of different Fourier methods for image retrieval. Image Vis. Comput. 123, 33–49 (2005)

    Article  Google Scholar 

  33. Zhu, S., Yull, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 884–900 (1996)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Qualifying High School, Alhidaya Islamiya, Ministry of National Education, Safi, Morocco

    Youssef Ait Khouya

  2. GRIFT Research Group, CRISTAL Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Faouzi Ghorbel

Authors
  1. Youssef Ait Khouya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faouzi Ghorbel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youssef Ait Khouya .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ait Khouya, Y., Ghorbel, F. (2018). Shape Classification Using B-spline Approximation and Contour Based Shape Descriptors. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76357-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76356-9

  • Online ISBN: 978-3-319-76357-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation