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Skeletonization Algorithm Using Discrete Contour Map

  • Hassan Id Ben IdderEmail author
  • Nabil Laachfoubi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

The skeleton of a binary object can be considered as an alternative to the object itself; it describes the object in a simple and compact manner that preserves the object topology. In this paper, we introduce a new definition for discrete contour curves, and we propose a new approach for extracting a well-shaped and connected skeleton of two-dimensional binary objects using a transformation of the distance map into contour map, which allows us to disregard the nature of the distance metric used. Indeed, our algorithm can support various distances such as the city-block distance, the chessboard distance, the chamfer distance or the Euclidean distance. To evaluate the proposed technique, experiments are conducted on shape benchmark dataset.

Keywords

Image analysis Digital topology Distance map Discrete Contour Map Skeletonization 

References

  1. 1.
    Abu-Ain, W., Abdullah, S.N.H.S., Bataineh, B., Abu-Ain, T., Omar, K.: Skeletonization Algorithm for Binary Images. Procedia Technology 11, 704–709 (2013)CrossRefGoogle Scholar
  2. 2.
    Andreadis, I., Vardavoulia, M.I., Louverdis, G., Papamarkos, N.: Colour image skeletonisation. In: Proceedings of the 10th European Signal Processing Conference, vol. 4, pp. 2389–2392 (2000)Google Scholar
  3. 3.
    Andres, E., Jacob, M.A.: The discrete analytical hyperspheres. IEEE Transactions on Visualization and Computer Graphics 3(1), 75–86 (1997)CrossRefGoogle Scholar
  4. 4.
    Arcelli, C., di Baja, G.: A one-pass two-operation process to detect the skeletal pixels on the 4-distance transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(4), 411–414 (1989)CrossRefGoogle Scholar
  5. 5.
    Arcelli, C., di Baja, G.S.: On the Sequential Approach to Medial Line Transformation. IEEE Transactions on Systems, Man and Cybernetics 8(2), 139–144 (1978)CrossRefGoogle Scholar
  6. 6.
    Arcelli, C.: Pattern thinning by contour tracing. Computer Graphics and Image Processing 17(2), 130–144 (1981)CrossRefGoogle Scholar
  7. 7.
    Arcelli, C., Di Baja, G.S.: A Width-Independent Fast Thinning Algorithm. IEEE Transactions on PAMI Pattern Analysis and Machine Intelligence 7(4), 463–474 (1985)Google Scholar
  8. 8.
    Arcelli, C., di Baja, G.S.: A contour characterization for multiply connected figures. Pattern Recognition Letters 6(4), 245–249 (1987)Google Scholar
  9. 9.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Models for the Perception of Speech and Visual Form, pp. 362–380 (1967)Google Scholar
  10. 10.
    Brandt, J.W., Algazi, V.: Continuous skeleton computation by Voronoi diagram. CVGIP: Image Understanding 55(3), 329–338 (1992)CrossRefzbMATHGoogle Scholar
  11. 11.
    Chaussard, J., Couprie, M., Talbot, H.: Robust skeletonization using the discrete \(\lambda \)-medial axis. Pattern Recognition Letters 32(9), 1384–1394 (2011)CrossRefGoogle Scholar
  12. 12.
    Choi, W.P., Lam, K.M., Siu, W.C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recognition 36(3), 721–729 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Cr, D., Rosenfeld, A.: Thinning algorithms for gray-scale picture. IEEE Trans. Pattern Anal. Mach. Intell. 1(1), 88–89 (1979)Google Scholar
  14. 14.
    Ge, Y., Fitzpatrick, J.M.: On the generation of skeletons from discrete Euclidean distance maps. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(11), 1055–1066 (1996)CrossRefGoogle Scholar
  15. 15.
    Hilditch, C.: An Application of Graph Theory in Fabric Design. Machine Intelligence 3, 325–347 (1968)zbMATHGoogle Scholar
  16. 16.
    Hilitch, C.J.: Linear skeletons from square cupboards. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 4, p. 403. Edinburgh University Press (1969)Google Scholar
  17. 17.
    Huang, L., Wan, G., Liu, C.: An improved parallel thinning algorithm. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, pp. 780–783 (August 2003)Google Scholar
  18. 18.
    Ji, X., Feng, J.: A new approach to thinning based on time-reversed heat conduction model (image processing). In: 2004 International Conference on Image Processing, ICIP 2004, vol. 1, pp. 653–656 (October 2004)Google Scholar
  19. 19.
    Latecki, L.J., Li, Q.N., Bai, X., Liu, W.Y.: Skeletonization using SSM of the distance transform. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 5, pp. V-349–V-352 (September 2007)Google Scholar
  20. 20.
    Leymarie, F., Levine, M.D.: Simulating the grassfire transform using an active contour model. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(1), 56–75 (1992)CrossRefGoogle Scholar
  21. 21.
    Montanvert, A.: Contribution au traitement de formes discrèes: squelettes et codage par graphe de la ligne médiane. Theses, Institut National Polytechnique de Grenoble - INPG; Université Joseph-Fourier - Grenoble I (October 1987)Google Scholar
  22. 22.
    Parker, J.R., Jennings, C., Molaro, D.: A force-based thinning strategy with sub-pixel precision. In: Vision Interface Conference, pp. 82–87 (1994)Google Scholar
  23. 23.
    Pavlidis, T.: A flexible parallel thinning algorithm. In: Proceedings of the International Conference on Pattern Recognition and Image Processing, pp. 162–167 (1981)Google Scholar
  24. 24.
    Pavlidis, T.: Algorithms for graphics and image processing. Digital system design series. Computer Science Press (1982)Google Scholar
  25. 25.
    Pavlidis, T.: A thinning algorithm for discrete binary images. Computer Graphics and Image Processing 13(2), 142–157 (1980)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Pavlidis, T.: An asynchronous thinning algorithm. Computer Graphics and Image Processing 20(2), 133–157 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Rosenfeld, A.: A characterization of parallel thinning algorithms. Information and Control 29(3), 286–291 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Sharvit, D., Chan, J., Tek, H., Kimia, B.B.: Symmetry-based Indexing of Image Databases. Journal of Visual Communication and Image Representation 9(4), 366–380 (1998)CrossRefGoogle Scholar
  29. 29.
    Shih, F.Y., Pu, C.C.: A skeletonization algorithm by maxima tracking on Euclidean distance transform. Pattern Recognition 28(3), 331–341 (1995)CrossRefGoogle Scholar
  30. 30.
    Shih, F.Y., Wu, Y.T.: Fast Euclidean distance transformation in two scans using a 3x3 neighborhood. Computer Vision and Image Understanding 93(2), 195–205 (2004)CrossRefGoogle Scholar
  31. 31.
    Wan, Y., Yao, L., Xu, B., Zeng, P.: A distance map based skeletonization algorithm and its application in fiber recognition. In: International Conference on Audio, Language and Image Processing, ICALIP 2008, pp. 1769–1774 (July 2008)Google Scholar
  32. 32.
    You, X., Tang, Y.Y.: Wavelet-Based Approach to Character Skeleton. IEEE Transactions on Image Processing 16(5), 1220–1231 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics, IR2M laboratory Faculty of Science and TechnologyUniversity Hassan 1stSettatMorocco

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