LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: World Scientific Publishing
Languages: English
Types: Part of book or chapter of book
Subjects: QA75
An overview is given on the use of cellular automata for image processing. We first consider the number of patterns that can exist in a neighbourhood, allowing for invariance to certain transformation. These patterns correspond to possible rules, and several schemes are described for automatically learning an appropriate rule set from training data. Two alternative schemes are given for coping with gray level (rather than binary) images without incurring a huge explosion in the number of possible rules. Finally, examples are provided of training various types of cellular automata with various rule identification schemes to perform several image processing tasks.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • LNAI 4578, pp. 404-411.
    • Sun, X., Rosin, P. and Martin, R. (2011). Fast rule identification and neighborhood selection for cellular automata, IEEE Transactions on Systems Man and Cybernetics, Part B: Cybernetics 41, 3, pp. 749-760.
    • Syswerda, G. (1989). Uniform crossover in genetic algorithms, in Proc. Third Int. Conf. on Genetic Algorithms (Lawrence Erlbaum Associates), pp. 2-9.
    • Terrazas, G., Siepmann, P., Kendall, G. and Krasnogor, N. (2007). An evolutionary methodology for the automated design of cellular automaton-based complex systems, J. Cellular Automata 2, 1, pp. 77-102.
    • Ulam, S. (1962). On some mathematical problems connected with patterns of growth of figures, in Proc. Symp. Appl. Math., Vol. 14, pp. 215-224.
    • von Neumann, J. (1966). Theory of Self-Reproducing Automata (University of Illinois Press).
    • Wang, L. and He, D. (1990). Texture classification using texture spectrum, Pattern Recognition 23, pp. 905-910.
    • Wang, Z., Bovik, A., Sheikh, H. and Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity, IEEE Trans. on Image Processing 13, 4, pp. 600-612.
    • Wolfram, S. (1994). Cellular Automata and Complexity Collected Papers (AddisonWesley).
    • Zhao, Y. and Billings, S. (2007). The identification of cellular automata, Journal of Cellular Automata 2, 1, pp. 47-65.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article