Image processing using 3-state cellular automata

Article English OPEN
Rosin, Paul L. (2010)

This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.
  • References (35)
    35 references, page 1 of 4

    [1] I. Kusch and M. Markus. Mollusc shell pigmentation: cellular automaton simulations and evidence for undecidability. J. Theor. Biol., 178:333-340, 1996.

    [2] P.P. Chaudhuri, D.R. Chowdhury, S. Nandi, and S. Chattopadhyay. Theory and Applications: Additive Cellular Automata. IEEE Press, 1997.

    [3] C. Georgoulas, L. Kotoulas, and G. Sirakoulis. Real-time disparity map computation module. J. Microprocessors and Microsystems, 32(3):159-170, 2008.

    [4] M. Mitchell, P.T. Hraber, and J.P. Crutchfield. Evolving cellular automata to perform computation: Mechanisms and impedients. Physica D, 75:361-391, 1994.

    [5] P. Sahota, M.F. Daemi, and D.G. Elliman. Training genetically evolving cellular automata for image processing. Proc. Speech, Image Processing and Neural Networks, 2:753-756, 1994.

    [6] A. Adamatzky. Automatic programming of cellular automata: identification approach. Kybernetes, 26(2):126-135, 1997.

    [7] M. Batouche, S. Meshoul, and A. Abbassene. On solving edge detection by emergence. In Int. Conf. on Industrial, Engineering and Other Apps. of Applied Intelligent Systems, volume LNAI 4031, pages 800-808, 2006.

    [8] S. Slatnia, M. Batouche, and K.E. Melkemi. Evolutionary cellular automata based-approach for edge detection. In Int. Workshop on Fuzzy Logic and Applications, volume LNAI 4578, pages 404-411, 2007.

    [9] A. Chavoya and Y. Duthen. Using a genetic algorithm to evolve cellular automata for 2D/3D computational development. In Genetic and Evolutionary Comp. Conf., pages 231-232, 2006.

    [10] R.V. Craiu and T.C.M. Lee. Pattern generation using likelihood inference for cellular automata. IEEE Trans. on Image Processing, 15(7):1718-1727, 2006.

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