Parallel Skeletonizing of Digital Images by Using Cellular Automata

  • Francisco Peña-Cantillana
  • Ainhoa Berciano
  • Daniel Díaz-Pernil
  • Miguel A. Gutiérrez-Naranjo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7309)


Recent developments of computer architectures together with alternative formal descriptions provide new challenges in the study of digital Images. In this paper we present a new implementation of the Guo & Hall algorithm [8] for skeletonizing images based on Cellular Automata. The implementation is performed in a real-time parallel way by using the GPU architecture. We show also some experiments of skeletonizing traffic signals which illustrates its possible use in real life problems.


Cellular Automaton Cellular Automaton Parallel Implementation Medial Axis Natural Computing 
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  1. 1.
    Arcelli, C., di Baja, G.S.: Euclidean skeleton via centre-of-maximal-disc extraction. Image and Vision Computing 11(3), 163–173 (1993)CrossRefGoogle Scholar
  2. 2.
    Attali, D., Boissonnat, J.D., Edelsbrunner, H.: Stability and Computation of Medial Axes - a State-of-the-Art Report. In: Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, ch. 6, pp. 109–125. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    di Baja, G.S., Thiel, E.: Skeletonization algorithm running on path-based distance maps. Image and Vision Computing 14(1), 47–57 (1996)CrossRefGoogle Scholar
  4. 4.
    Biasotti, S., Attali, D., Boissonnat, J.-D., Edelsbrunner, H., Elber, G., Mortara, M., Baja, G.S., Spagnuolo, M., Tanase, M., Veltkamp, R.: Skeletal structures. In: Floriani, L., Spagnuolo, M. (eds.) Shape Analysis and Structuring. Mathematics and Visualization, pp. 145–183. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Blum, H.: An associative machine for dealing with the visual field and some of its biological implications. Computer and Mathematical Sciences Laboratory, Electronics Research Directorate, Air Force Cambridge Research Laboratories, Office of Aerospace Research, United States Air Force (1962)Google Scholar
  6. 6.
    Blum, H.: An associative machine for dealing with the visual field and some of its biological implications. In: Bernard, E.E., Kare, M.R. (eds.) Biological Prototypes and Synthetic Systems, vol. 1, pp. 244–260. Plenum Press, New York (1962); Proceedings of the 2nd Annual Bionics Symposium, held at Cornell University (1961)Google Scholar
  7. 7.
    Bräunl, T.: Parallel image processing. Springer (2001)Google Scholar
  8. 8.
    Guo, Z., Hall, R.W.: Parallel thinning with two-subiteration algorithms. Communications of the ACM 32, 359–373 (1989)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hernandez, G., Herrmann, H.J.: Cellular-automata for elementary image-enhancement. Graphical Models and Image Processing 58(1), 82–89 (1996)CrossRefGoogle Scholar
  10. 10.
    Kari, J.: Theory of cellular automata: A survey. Theoretical Computer Science 334(1-3), 3–33 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Kari, L., Rozenberg, G.: The many facets of natural computing. Communications of the ACM 51(10), 72–83 (2008)CrossRefGoogle Scholar
  12. 12.
    Kauffmann, C., Piché, N.: A cellular automaton framework for image processing on GPU. In: Yin, P.Y. (ed.) Pattern Recoginition, pp. 353–375. InTech (2009)Google Scholar
  13. 13.
    Klette, R., Ahn, J., Haeusler, R., Herman, S., Huang, J., Khan, W., Manoharan, S., Morales, S., Morris, J., Nicolescu, R., Ren, F., Schauwecker, K., Yang, X.: Advance in vision-based driver assistance. In: 2011 International Conference on Electric Technology and Civil Engineering (ICETCE), pp. 987–990 (April 2011)Google Scholar
  14. 14.
    Mohapatra, A.G.: Computer vision based smart lane departure warning system for vehicle dynamics control. Sensors & Transducers Journal 132(9), 122–135 (2011)MathSciNetGoogle Scholar
  15. 15.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Computer Graphics Forum 26(1), 80–113 (2007)CrossRefGoogle Scholar
  16. 16.
    Saeed, K., Tabedzki, M., Rybnik, M., Adamski, M.: K3M: A universal algorithm for image skeletonization and a review of thinning techniques. Applied Mathematics and Computer Science 20(2), 317–335 (2010)zbMATHGoogle Scholar
  17. 17.
    de Saint Pierre, T., Milgram, M.: New and efficient cellular algorithms for image processing. CVGIP: Image Understanding 55(3), 261–274 (1992)zbMATHCrossRefGoogle Scholar
  18. 18.
    Selvapeter, P.J., Hordijk, W.: Cellular automata for image noise filtering. In: NaBIC, pp. 193–197. IEEE (2009)Google Scholar
  19. 19.
    Siddiqi, K., Pizer, S.M.: Medial representations: mathematics, algorithms and applications. In: Computational Imaging and Vision. Springer (2008)Google Scholar
  20. 20.
    Wolfram, S.: Cellular Automata and Complexity: Collected Papers. Perseus Books Group (1994)Google Scholar
  21. 21.
    NVIDIA Corporation. NVIDIA CUDAtm Programming Guide,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Peña-Cantillana
    • 1
  • Ainhoa Berciano
    • 2
    • 3
  • Daniel Díaz-Pernil
    • 3
  • Miguel A. Gutiérrez-Naranjo
    • 1
  1. 1.Research Group on Natural Computing, Department of Computer Science and Artificial IntelligenceUniversity of SevilleSpain
  2. 2.Departamento de Didáctica de la Matemática y de las Ciencias ExperimentalesUniversity of the Basque CountrySpain
  3. 3.Research Group on Computational Topology and Applied Mathematics, Department of Applied MathematicsUniversity of SevilleSpain

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