Training Cellular Automata for Image Processing

  • Paul L. Rosin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward search used to select good rule sets for a range of tasks, namely: noise filtering, thinning, and convex hulls. Several modifications to the standard CA formulation were made (the B-rule and 2-cycle CAs) which were found to improve performance.

Keywords

Root Mean Square Error Root Mean Square Convex Hull Cellular Automaton Cellular Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Andre, D., Bennett III, F.H., Koza, J.R.: Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem. In: Proc. Genetic Prog., pp. 3–11. MIT Press, Cambridge (1996)Google Scholar
  2. 2.
    Borgefors, G., di Baja, G.S.: Analysing nonconvex 2D and 3D patterns. Computer Vision and Image Understanding 63(1), 145–157 (1996)CrossRefGoogle Scholar
  3. 3.
    Chen, T., Wu, H.R.: Application of partition-based median type filters for suppressing noise in images. IEEE Trans. Image Proc. 10(6), 829–836 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    de Saint Pierre, T., Milgram, M.: New and efficient cellular algorithms for image processing. CVGIP: Image Understanding 55(3), 261–274 (1992)MATHCrossRefGoogle Scholar
  5. 5.
    Dyer, C.R., Rosenfeld, A.: Parallel image processing by memory-augmented cellular automata. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(1), 29–41 (1981)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Ganguly, N., Sikdar, B.K., Deutsch, A., Canright, G., Chaudhuri, P.P.: A survey on cellular automata. Technical Report 9, Centre for High Performance Computing, Dresden University of Technology (2003)Google Scholar
  7. 7.
    Gardner, M.: The fantastic combinations of john conway’s new solitaire game “life”. Scientific American, 120–123 (1970)Google Scholar
  8. 8.
    Hernandez, G., Herrmann, H.J.: Cellular automata for elementary image enhancement. Graphical Models and Image Processing 58(1), 82–89 (1996)CrossRefGoogle Scholar
  9. 9.
    Lam, L., Suen, C.Y.: An evaluation of parallel thinning algorithms for character-recognition. IEEE Trans. PAMI 17(9), 914–919 (1995)Google Scholar
  10. 10.
    Jiménez Morales, F., Crutchfield, J.P., Mitchell, M.: Evolving 2-d cellular automata to perform density classification. Parallel Comp. 27, 571–585 (2001)MATHCrossRefGoogle Scholar
  11. 11.
    Preston, K., Duff, M.J.B.: Modern Cellular Automata-Theory and Applications. Plenum Press, New York (1984)MATHGoogle Scholar
  12. 12.
    Pudil, P., Novovicova, J., Kittler, J.V.: Floating search methods in feature-selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)CrossRefGoogle Scholar
  13. 13.
    Sipper, M.: The evolution of parallel cellular machines toward evolware. BioSystems 42, 29–43 (1997)CrossRefGoogle Scholar
  14. 14.
    Viher, B., Dobnikar, A., Zazula, D.: Cellular automata and follicle recognition problem and possibilities of using cellular automata for image recognition purposes. Int. J. of Medical Informatics 49(2), 231–241 (1998)CrossRefGoogle Scholar
  15. 15.
    von Neumann, J.: Theory of Self-Reproducing Automata. University of Illinois Press, US (1966)Google Scholar
  16. 16.
    Wolfram, S.: Cellular Automata and Complexity. Addison-Wesley, Reading (1994)MATHGoogle Scholar
  17. 17.
    Yu, D., Ho, C., Yu, X., Mori, S.: On the application of cellular automata to image thinning with cellular neural network. In: Cellular Neural Networks and their Applications, pp. 210–215 (1992)Google Scholar
  18. 18.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Comm. ACM 27(3), 236–240 (1984)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Paul L. Rosin
    • 1
  1. 1.Cardiff School of Computer ScienceCardiff UniversityCardiffUK

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