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NURBS Skeleton: A New Shape Representation Scheme Using Skeletonization and NURBS Curves Modeling

  • Mohamed Naouai
  • Atef Hammouda
  • Sawssen Jalel
  • Christiane Weber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

The representation and description of shapes or regions that have been segmented out of an image are early steps in the operation of most Computer vision systems; they serve as a precursor to several possible higher level tasks such as object/character recognition. In this context, skeletons have good properties for data reduction and representation. In this paper we present a novel shape representation scheme, named ”NURBS Skeleton”, based on the thinning medial axis method, the pruning process and the Non Uniform Rational B-Spline (NURBS) curves approximation for the modeling step.

Keywords

Skeleton shape description Medial Axis Transform (MAT) NURBS curves 

References

  1. 1.
    Mang, C., Yun-cai, L.: Connection Skeleton Extraction Based on Contour Connectedness. Shanghai Jiaotong Univ. (Sci.) 13(5), 521–527 (2008)CrossRefGoogle Scholar
  2. 2.
    Pavaldis, T.: A review of algorithms for shape analysis. Computer Graphics and Image Processing 7, 243–258 (1978)CrossRefGoogle Scholar
  3. 3.
    Kimmel, R.: Skeletonization via distance maps and level sets. Computer Vision and Image Understanding 62(3), 382–391 (1995)CrossRefGoogle Scholar
  4. 4.
    Brandt, J.W., Algazi, V.R.: Continuous skeleton computation by voronoi diagram. CVGIP: Image Understanding 55, 329–338 (1994)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lam, L., Lee, S.-W., Suen, C.Y.: Thinning Methodologies-A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(9), 879 (1992)CrossRefGoogle Scholar
  6. 6.
    Piegl, L., Tiller, W.: The NURBS Book. 2nd edn.Google Scholar
  7. 7.
    Messmer, B.T., Bunke, H.: EFcient subgraph isomorphism detection: a decomposition approach. IEEE Trans. Knowledge Data Eng. 12(2), 307–323 (2000)CrossRefGoogle Scholar
  8. 8.
    Ruberto, D., Rodriguez, G., Casta, L.: Recognition of shapes bymorphological attributed relational graphs. In: Atti dellVIII Convegno AIIA 2002, Siena, Italy (2002); Messmer, B.T., Bunke, H.: EFcient subgraph isomorphismGoogle Scholar
  9. 9.
    Liu, T.L., Geiger, D., Kohn, R.V.: Representation and self similarity of shape. In: Proceedings of the Sixth International Conference on Computer Vision, pp. 1129–1135. IEEE Computer Society, Washington (1999)Google Scholar
  10. 10.
    Niranjan, M., Rajan, V.T.: An efficient shape representation scheme using Voronoi skeletons. Pattern Recognition Letters 16, 147–160 (1995)CrossRefGoogle Scholar
  11. 11.
    Ogniewicz, R.L., Kbler, O.: Hierarchic Voronoi Skeletons. Pattern Recognition 28(3), 343–359 (1995)CrossRefGoogle Scholar
  12. 12.
    Couprie, M., Coeurjolly, D., Zrour, R.: Discrete bisector function and Euclidian skeleton in 2D and 3D. Image and Vision Computing 25, 1543–1556 (2007)CrossRefGoogle Scholar
  13. 13.
    Borgefors, G.: Distance Transforms in Digital Images. Computer Vision, Graphics and Image Processing 34, 344–371 (1986)CrossRefGoogle Scholar
  14. 14.
    Chiang, J.Y., Tue, S.C., Leu, Y.C.: A New Algorithm for Line Image Vectorization. Pattern Recognition 31(10), 1541–1549 (1998)CrossRefGoogle Scholar
  15. 15.
    Niblack, C.W., Gibbons, P.B., Capson, D.W.: Generating Skeletons and Centerlines from the Distance Transform. CVGIP: Graphical Models and Image Processing 54(5), 420–437 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohamed Naouai
    • 1
    • 2
  • Atef Hammouda
    • 1
  • Sawssen Jalel
    • 1
  • Christiane Weber
    • 2
  1. 1.Faculty of Science of TunisUniversity campus el Manar DSI 2092 Tunis Belvdaire-Tunisia Research unit UrpahFrance
  2. 2.Laboratory Image and Ville UMR7011-CNRSUniversity Strasbourg 3StrasbourgFrance

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