A Novel 2D Contour Description Generalized Curvature Scale Space

  • Ameni BenkhlifaEmail author
  • Faouzi GhorbelEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 684)


Here, we intend to propose a 2D contour descriptor that we call Generalized Curvature Scale Space (GCSS) based on the iso-curvature levels, and the curvature scale space (CSS) descriptor. We start by computing the curvature in different scales and extract the points which have the same curvature values as the maximums in each scale. Each CSS image is represented by a set of key points. The Dynamic Time Warping (DTW) similarity measure is used. We reach a significant rate in image recognition using two data sets (HMM GPD and MPEG7 CE Shape-1 Part-B set).


Pattern recognition Contour 2D Curvature Iso-curvature CSS 


  1. 1.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Robust and efficient shape indexing through curvature scale space. In: Proceedings of the 1996 British Machine and Vision Conference BMVC, vol. 96 (1996)Google Scholar
  2. 2.
    Wallace, T.P., Wintz, P.A.: An efficient three-dimensional aircraft recognition algorithm using normalized Fourier descriptors. Comput. Graph. Image Process. 13(2), 99–126 (1980)CrossRefGoogle Scholar
  3. 3.
    Persoon, E., King-Sun, F.: Shape discrimination using Fourier descriptors. IEEE Trans. Syst. Man. Cybern. 7(3), 170–179 (1977)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Ghorbel F.: Stability of invariants Fourrier descriptors and its interference on the shape classification. In: 11th International Conference on Pattern Recognition, (ICPR), 30 August–3 September, The Hague (1992)Google Scholar
  5. 5.
    Ghorbel, F.: Towards a unitary formulation for invariant image description: application to image coding. Annales des telecommunications 53(5–6), 242 (1998). SpringerGoogle Scholar
  6. 6.
    Hoffman, D.D., Richards, W.A.: Parts of recognition. Cognition 18(1), 65–96 (1984)CrossRefGoogle Scholar
  7. 7.
    Siddiqi, K., Kimia, B.B.: Parts of visual form: computational aspects. IEEE Trans. Pattern Anal. Mach. Intell. 17(3), 239–251 (1995)CrossRefGoogle Scholar
  8. 8.
    Shu, X., Xiao-Jun, W.: A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis. Comput. 29(4), 286–294 (2011)CrossRefGoogle Scholar
  9. 9.
    Xu, C., Liu, J., Tang, X.: 2D shape matching by contour flexibility. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 180–186 (2009)CrossRefGoogle Scholar
  10. 10.
    Klassen, E., et al.: Analysis of planar shapes using geodesic paths on shape spaces. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 372–383 (2004)CrossRefGoogle Scholar
  11. 11.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)CrossRefGoogle Scholar
  12. 12.
    Hong, B.-W., et al.: Shape representation based on integral kernels: application to image matching and segmentation. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1. IEEE (2006)Google Scholar
  13. 13.
    Chen, Y.W., Xu, C.L.: Rolling penetrate descriptor for shape-based image retrieval and object recognition. Pattern Recognit. Lett. 30(9), 799–804 (2009)CrossRefGoogle Scholar
  14. 14.
    Sankoff, D., Kruskal, J.B.: Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. In: David, S., Kruskal, J.B. (eds.), vol. 1. Addison-Wesley Publication, Reading (1983)Google Scholar
  15. 15.
    Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (2000)Google Scholar
  16. 16.
    Bicego, M., Murino, V., Figueiredo, M.A.T.: Similarity-based classification of sequences using hidden Markov models. Pattern Recognit. 37(12), 2281–2291 (2004)CrossRefzbMATHGoogle Scholar
  17. 17.
    Thakoor, N., Gao, J., Jung, S.: Hidden Markov model-based weighted likelihood discriminant for 2-D shape classification. IEEE Trans. Image Process. 16(11), 2707–2719 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Cristal Laboratory, Grift Group, National School of Computer SciencesUniversité de la ManoubaManoubaTunisia

Personalised recommendations