Parallel Thinning with Complex Objects and Actors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8961)

Abstract

Based on our earlier complex objects proposal, we present three novel concurrent membrane computing models for a fundamental image processing task: the thinning (or skeletonisation) of binary images, based on the classical Guo-Hall algorithm (A2). The first model is synchronous and uses one cell per pixel and relies on inter-cell parallelism; the second model is an asynchronous version of the first; the third model uses one single cell, with one sub-cellular object per pixel, and relies on intra-cell parallelism. The static and dynamic qualities of our models validate our complex objects proposal: (i) the proposed models are crisp (comparable to the best pseudocode); and (ii) complex objects concurrency and messaging can be efficiently emulated on a message-based Actors framework (which opens a novel research path).

Keywords

Membrane computing P systems Inter-cell parallelism Intra-cell parallelism Prolog terms Complex objects Generic rules Image processing Guo-Hall algorithm Parallel and concurrent models Synchronous and asynchronous models Termination detection Message-based Actor model 

References

  1. 1.
    Agha, G., Thati, P.: An algebraic theory of actors and its application to a simple object-based language. In: Owe, O., Krogdahl, S., Lyche, T. (eds.) From OO to FM (Dahl Festschrift). LNCS, vol. 2635, pp. 26–57. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bălănescu, T., Nicolescu, R., Wu, H.: Asynchronous P systems. International Journal of Natural Computing Research 2(2), 1–18 (2011)CrossRefGoogle Scholar
  3. 3.
    Díaz-Pernil, D., Peña-Cantillana, F., Gutiérrez-Naranjo, M.A.: A parallel algorithm for skeletonizing images by using spiking neural P systems. Neurocomputing 115, 81–91 (2013)CrossRefGoogle Scholar
  4. 4.
    Dinneen, M.J., Kim, Y.-B., Nicolescu, R.: A faster P solution for the Byzantine agreement problem. In: Gheorghe, M., Hinze, T., Păun, Gh., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 175–197. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Dinneen, M.J., Kim, Y.B., Nicolescu, R.: P systems and the Byzantine agreement. Journal of Logic and Algebraic Programming 79(6), 334–349 (2010)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    ElGindy, H., Nicolescu, R., Wu, H.: Fast distributed DFS solutions for edge-disjoint paths in digraphs. In: Csuhaj-Varjú, E., Gheorghe, M., Rozenberg, G., Salomaa, A., Vaszil, Gy. (eds.) CMC 2012. LNCS, vol. 7762, pp. 173–194. Springer, Heidelberg (2013). http://dx.doi.org/10.1007/978-3-642-36751-9_13 CrossRefGoogle Scholar
  7. 7.
    Floyd, R.W.: Nondeterministic algorithms. J. ACM 14(4), 636–644 (1967). http://doi.acm.org/10.1145/321420.321422 CrossRefMATHGoogle Scholar
  8. 8.
    Gimelfarb, G., Nicolescu, R., Ragavan, S.: P systems in stereo matching. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 285–292. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-23678-5_33 CrossRefGoogle Scholar
  9. 9.
    Gimelfarb, G., Nicolescu, R., Ragavan, S.: P system implementation of dynamic programming stereo. Journal of Mathematical Imaging and Vision 47(1–2), 13–26 (2013). http://dx.doi.org/10.1007/s10851-012-0367-6 CrossRefGoogle Scholar
  10. 10.
    Guo, Z., Hall, R.W.: Parallel thinning with two-subiteration algorithms. Commun. ACM 32(3), 359–373 (1989). http://doi.acm.org/10.1145/62065.62074 CrossRefMathSciNetGoogle Scholar
  11. 11.
    Hewitt, C.: Viewing control structures as patterns of passing messages. Artificial Intelligence 8(3), 323–364 (1977). http://www.sciencedirect.com/science/article/pii/0004370277900339 CrossRefGoogle Scholar
  12. 12.
    Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers Inc., San Francisco (1996)MATHGoogle Scholar
  13. 13.
    Nicolescu, R.: Parallel and distributed algorithms in P systems. In: Gheorghe, M., Păun, Gh., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 35–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Nicolescu, R., Gimelfarb, G., Morris, J., Gong, R., Delmas, P.: Regularising ill-posed discrete optimisation: Quests with P systems. Fundam. Inf. 131(3–4), 465–483 (2014)MATHMathSciNetGoogle Scholar
  15. 15.
    Nicolescu, R., Ipate, F., Wu, H.: Programming P systems with complex objects. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Yu., Rozenberg, G., Salomaa, A. (eds.) CMC 2013. LNCS, vol. 8340, pp. 280–300. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  16. 16.
    Nicolescu, R., Ipate, F., Wu, H.: Towards high-level P systems programming using complex objects. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Yu. (eds.) 14th International Conference on Membrane Computing, CMC14, Chişinău, Moldova, August 20-23, 2013, Proceedings, pp. 255–276. Institute of Mathematics and Computer Science, Academy of Sciences of Moldova, Chişinău (2013)Google Scholar
  17. 17.
    Nicolescu, R., Wu, H.: BFS solution for disjoint paths in P systems. In: Calude, C.S., Kari, J., Petre, I., Rozenberg, G. (eds.) UC 2011. LNCS, vol. 6714, pp. 164–176. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-21341-0_20 CrossRefGoogle Scholar
  18. 18.
    Nicolescu, R., Wu, H.: New solutions for disjoint paths in P systems. Natural Computing 11, 637–651 (2012). http://dx.doi.org/10.1007/s11047-012-9342-9 CrossRefMathSciNetGoogle Scholar
  19. 19.
    Nicolescu, R., Wu, H.: Complex objects for complex applications. Romanian Journal of Information Science and Technology (2014, to appear)Google Scholar
  20. 20.
    Peña-Cantillana, F., Berciano, A., Díaz-Pernil, D., Gutiérrez-Naranjo, M.A.: Parallel skeletonizing of digital images by using cellular automata. In: Ferri, M., Frosini, P., Landi, C., Cerri, A., Di Fabio, B. (eds.) CTIC 2012. LNCS, vol. 7309, pp. 39–48. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Păun, Gh., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press Inc., New York (2010)CrossRefMATHGoogle Scholar
  22. 22.
    Reina-Molina, R., Díaz-Pernil, D.: Bioinspired parallel 2D or 3D skeletonization. IMAGEN-A 3(5), 41–44 (2013)Google Scholar
  23. 23.
    Reina-Molina, R., Díaz-Pernil, D., Gutiérrez-Naranjo, M.A.: Cell complexes and membrane computing for thinning 2D and 3D images. In: del Amor, M.A.M., Păun, Gh., Pérez-Hurtado, I., Romero-Campero, F.J. (eds.) Tenth Brainstorming Week on Membrane Computing. RGNC REPORT, vol. 1, pp. 91–110. Universidad de Sevilla (2012)Google Scholar
  24. 24.
    Syme, D., Granicz, A., Cisternino, A.: Expert F# 3.0, 3rd edn. Apress, Berkely (2012)CrossRefGoogle Scholar
  25. 25.
    Wu, H.: Minimum spanning tree in P systems. In: Pan, L., Păun, Gh., Song, T. (eds.) Proceedings of the Asian Conference on Membrane Computing (ACMC2012), pp. 88–104. Huazhong University of Science and Technology, Wuhan (2012)Google Scholar
  26. 26.
    Wu, H.: Distributed Algorithms in P Systems. Ph.D. thesis, The University of Auckland, Auckland, New Zealand (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

Personalised recommendations