Skeleton Pruning by Contour Partitioning

  • Xiang Bai
  • Longin Jan Latecki
  • Wen-Yu Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4245)


In this paper, we establish a unique correspondence between skeleton branches and subarcs of object contours. Based on this correspondence, a skeleton is pruned by removing skeleton branches whose generating points are on the same contour subarc. This has an effect of removing redundant skeleton branches and retaining all the necessary visual branches. We show that this approach preserves skeleton topology, does not shift the skeleton, and it does not shrink the remaining branches.


Skeleton skeleton pruning discrete curve evolution 


  1. 1.
    Blum, H.: Biological Shape and Visual Science (Part I). J. Theoretical Biology 38, 205–287 (1973)CrossRefGoogle Scholar
  2. 2.
    Ogniewicz, R.L., Kübler, O.: Hierarchic Voronoi skeletons. Pattern Recognition 28(3), 343–359 (1995)CrossRefGoogle Scholar
  3. 3.
    Malandain, G., Fernandez-Vidal, S.: Euclidean skeletons. Image and Vision Computing 16, 317–327 (1998)CrossRefGoogle Scholar
  4. 4.
    Choi, W.-P., Lam, K.-M., Siu, W.-C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recognition 36, 721–729 (2003)zbMATHCrossRefGoogle Scholar
  5. 5.
    Pudney, C.: Distance-Ordered Homotopic Thinning: A Skeletonization Algorithm for 3D Digital Images. Computer Vision and Image Understanding 72(3), 404–413 (1998)CrossRefGoogle Scholar
  6. 6.
    Leymarie, F., Levine, M.: Simulating the grassfire transaction form using an active Contour model. IEEE Trans. PAMI 14(1), 56–75 (1992)Google Scholar
  7. 7.
    Golland, P., Grimson, E.: Fixed topology skeletons. CVPR 1, 10–17 (2000)Google Scholar
  8. 8.
    Mayya, N., Rajan, V.T.: Voronoi Diagrams of polygons: A framework for Shape Representation. In: Proc. of the IEEE CVPR, pp. 638–643 (1994)Google Scholar
  9. 9.
    Ge, Y., Fitzpatrick, J.M.: On the Generation of Skeletons from Discrete Euclidean Distance Maps. IEEE Trans. PAMI 18(11), 1055–1066 (1996)Google Scholar
  10. 10.
    Gold, C.M., Thibault, D., Liu, Z.: Map Generalization by Skeleton Retraction. In: ICA Workshop on Map Generalization, Ottawa (August 1999)Google Scholar
  11. 11.
    Latecki, L.J., Lakämper, R.: Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution. Computer Vision and Image Understanding (CVIU) 73, 441–454 (1999)CrossRefGoogle Scholar
  12. 12.
    Latecki, L.J., Lakämper, R.: Polygon evolution by vertex deletion. In: Nielsen, M., Johansen, P., Fogh Olsen, O., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, p. 398. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. 13.
    Latecki, L.J., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 22(10), 1185–1190 (2000)CrossRefGoogle Scholar
  14. 14.
    Latecki, L.J., Lakamper, R.: Application of planar shape comparison to object retrieval in image databases. Pattern Recognition 35(1), 15–29 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34(3), 344–371 (1986)CrossRefGoogle Scholar
  16. 16.
    Shaken, D., Bruckstein, A.M.: Pruning Medial Axes. Computer Vision and Image Understanding 69(2), 156–169 (1998)CrossRefGoogle Scholar
  17. 17.
    Dimitrov, P., Damon, J.N., Siddiqi, K.: Flux Invariants for Shape. In: CVPR (2003)Google Scholar
  18. 18.
    Latecki, L.J., Ghadially, R.-R., Lakämper, R., Eckhardt, U.: Continuity of the discrete curve evolution. Journal of Electronic Imaging 9(3), 317–326 (2000)CrossRefGoogle Scholar
  19. 19.
    Dimitrov, P., Phillips, C., Siddiqi, K.: Robust and Efficient Skeletal Graphs. In: CVPR, pp. 1417–1423 (2000)Google Scholar
  20. 20.
    Siddiqi, K., Bouix, S., Tannenbaum, A.R., Zucker, S.W.: Hamilton-Jacobi Skeletons. International Journal of Computer Vision 48(3), 215–231 (2002)zbMATHCrossRefGoogle Scholar
  21. 21.
    Vasilevskiy, A., Siddiqi, K.: Flux Maximizing Geometric Flows. IEEE Trans. PAMI 24(12), 1565–1578 (2002)Google Scholar
  22. 22.
    Brandt, J.W., Algazi, V.R.: Continuous skeleton computation by Voronoi diagram. Comput. Vision, Graphics, Image Processing 55, 329–338 (1992)zbMATHGoogle Scholar
  23. 23.
    Choi, H.I., Choi, S.W., Moon, H.P.: Mathematical Theory of Medial Axis Transform. Pacific Journal of Mathematics 181(1), 57–88 (1997)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Arcelli, C., Sanniti di Baja, G.: Euclidean skeleton via center of maximal disk extraction. Image and Vision Computing 11, 163–173 (1993)CrossRefGoogle Scholar
  25. 25.
    Kimmel, R., et al.: Skeletonization via Distance Maps and Level Sets. CVIU: Comp. Vision and Image Understanding 62(3), 382–391 (1995)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. PAMI 26(5), 550–571 (2004)Google Scholar
  27. 27.
    Latecki, L.J., Lakamper, R., Eckhardt, U.: Shape Descriptors for Non-rigid Shapes with a Single Closed Contour. In: Proc. CVPR (2000)Google Scholar
  28. 28.
    Pizer, S.M., Oliver, W.R., Bloomberg, S.H.: Hierarchial shape description via the multiresolution symmetric axis transform. IEEE Trans. PAMI 9, 505–511 (1987)Google Scholar
  29. 29.
    Borgefors, G., Ramella, G., Sanniti di Baja, G.: Hierarchical decomposition of multiscale skeletons. IEEE Trans. PAMI 13(11), 1296–1312 (2001)Google Scholar
  30. 30.
    Sanniti di Baja, G.: Well-shaped, stable and reversible skeletons from the (3,4)-distance transform. Visual Communication and Image Representation 5, 107–115 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiang Bai
    • 1
    • 2
  • Longin Jan Latecki
    • 1
  • Wen-Yu Liu
    • 2
  1. 1.CIS Dept.Temple UniversityPhiladelphiaUSA
  2. 2.Dept of Electronics & Information EngineeringHuazhong University of Sci. &, Tech.Wuhan, HubeiP.R. China

Personalised recommendations