INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.7, No.12, Nov. 2015

Selection of Optimum Rule Set of Two Dimensional Cellular Automata for Some Morphological Operations

Full Text (PDF, 536KB), PP.50-58


Views:57   Downloads:1

Author(s)

Anand Prakash Shukla, Suneeta Agarwal

Index Terms

Cellular Automata;Misclassification Error;Sequential Floating Forward Search;Thinning;Thickening;Morphological Operations

Abstract

The cellular automaton paradigm is very appealing and its inherent simplicity belies its potential complexity. Two dimensional cellular automata are significantly applying to image processing operations. This paper describes the application of cellular automata (CA) to various morphological operations such as thinning and thickening of binary images. The description about the selection of the optimum rule set of two dimensions cellular automata for thinning and thickening of binary images is illustrated by this paper. The selection of the optimum rule set from large search space has been performed on the basis of sequential floating forward search method. The misclassification error between the images obtained by the standard function and the one obtained by cellular automata rule is used as the fitness function. The proposed method is also compared with some standard methods and found suitable for the purpose of morphological operations.

Cite This Paper

Anand Prakash Shukla, Suneeta Agarwal,"Selection of Optimum Rule Set of Two Dimensional Cellular Automata for Some Morphological Operations", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.12, pp.50-58, 2015. DOI: 10.5815/ijitcs.2015.12.06

Reference

[1]S. Ulam, “Some ideas and prospects in biomathematics,” Annual review of biophysics and bioengineering, vol. 1, no. 1, pp. 277–292, 1972.

[2]J. Von Neumann, A. W. Burks et al., “Theory of self-reproducing automata,” 1966.

[3]S. Wolfram, A new kind of science. Wolfram media Champaign, 2002, vol. 5.

[4]M. Sipper, “The evolution of parallel cellular machines: Toward evol- ware,” BioSystems, vol. 42, no. 1, pp. 29–43, 1997.

[5]M. Gardner, “Mathematical games: The fantastic combinations of john conways new solitaire game life,” Scientific American, vol. 223, no. 4, pp. 120–123, 1970.

[6]H. De Garis, “Cam-brain the evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolves at electronic speeds inside a cellular automata machine (cam),” in Towards evolvable hardware. Springer, 1996, pp. 76–98.

[7]S. Aassine and M. C. El Ja?, “Vegetation dynamics modelling: a method for coupling local and space dynamics,” Ecological modelling, vol. 154, no. 3, pp. 237–249, 2002.

[8]R. Smith, “The application of cellular automata to the erosion of landforms,” Earth Surface Processes and Landforms, vol. 16, no. 3, pp. 273–281, 1991.

[9]C.-l. Chang, Y.-j. Zhang, and Y.-Y. Gdong, “Cellular automata for edge detection of images,” in Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, vol. 6. IEEE, 2004, pp. 3830–3834.

[10]T. Kumar and G. Sahoo, “A novel method of edge detection using cellular  automata,” International Journal  of  Computer Applications, vol. 9, no. 4, pp. 0975–8887, 2010.

[11]S. Wongthanavasu and R. Sadananda, “Pixel-level edge detection using a cellular automata-based model,” Advances in Intelligent Systems: Theory and Applications, vol. 59, pp. 343–351, 2000.

[12]M. Fukui and Y. Ishibashi, “Traffic flow in 1d cellular automaton model including cars moving with high speed,” Journal of the Physical Society of Japan, vol. 65, no. 6, pp. 1868–1870, 1996.

[13]A. Popovici and D. Popovici, “Cellular automata in image processing,” in Fifteenth International Symposium on Mathematical Theory of Net- works and Systems, vol. 1, 2002.

[14]F. M. Marchese, “A directional diffusion algorithm on cellular automata for robot path-planning,” Future Generation Computer Systems, vol. 18, no. 7, pp. 983–994, 2002.

[15]S. Nandi, B. Kar, and P. Pal Chaudhuri, “Theory and applications of cellular automata in cryptography,” Computers, IEEE Transactions on, vol. 43, no. 12, pp. 1346–1357, 1994.

[16]L. S. Davis, “A survey of edge detection techniques,” Computer graphics and image processing, vol. 4, no. 3, pp. 248–270, 1975.

[17]P. L. Rosin, “Training cellular automata for image processing,” Image Processing, IEEE Transactions on, vol. 15, no. 7, pp. 2076–2087, 2006.

[18]A P Shukla, S Chauhan and S Agarwal “Training of cellular automata for image filtering,” in Proc. Second International Conference on Advances in Computer Science and Application - CSA 2013,LNCS, pp. 86–95,2013.

[19]Y. Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in nd images,” in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, vol. 1. IEEE, 2001, pp. 105–112.

[20]M. Mitchell, J. P. Crutchfield, R. Das et al., “Evolving cellular automata with genetic algorithms: A review of recent work,” in Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA96), 1996.

[21]G. Sahoo, T. Kumar, B. Raina, and C. Bhatia, “Text extraction and enhancement of binary images using cellular automata,” International Journal of Automation and Computing, vol. 6, no. 3, pp. 254–260, 2009.

[22]P. Somol, P. Pudil, and J. Kittler, “Fast branch & bound algorithms for optimal feature selection,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 26, no. 7, pp. 900–912, 2004.

[23]A. Jain and D. Zongker, “Feature selection: Evaluation, application, and small sample performance,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, no. 2, pp. 153–158, 1997.

[24]D. Andre, F. H. Bennett III, and J. R. Koza, “Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem,” in Proceedings of the First Annual Conference on Genetic Programming. MIT Press, 1996, pp.3–11.

[25]H. Hao, C.-L. Liu, and H. Sako, “Comparison of genetic algorithm and sequential search methods for classifier subset selection.” in ICDAR. Citeseer, 2003, pp. 765–769.

[26]A. Adamatzky, “Automatic programming of cellular automata: identifi- cation approach,” Kybernetes, vol. 26, no. 2, pp. 126–135, 1997.

[27]M. Mitchell, P. Hraber, and J. P. Crutchfield, “Revisiting the edge of chaos: Evolving cellular automata to perform computations,” arXiv preprint adap-org/9303003, 1993.

[28]B. Straatman, R. White, and G. Engelen, “Towards an automatic calibration procedure for constrained cellular automata,” Computers, Environment and Urban Systems, vol. 28, no. 1, pp. 149–170, 2004.

[29]W. A. Yasnoff, J. K. Mui, and J. W. Bacus, “Error measures for scene segmentation,” Pattern Recognition, vol. 9, no. 4, pp. 217–231, 1977.

[30]Heidari, Hadis, Abdolah Chalechale, and Alireza Ahmadi Mohammadabadi. "Parallel Implementation of Color Based Image Retrieval Using CUDA on the GPU." International Journal of Information Technology and Computer Science (IJITCS) 6.1 (2013): 33.

[31]Goswami, Mr Saptarsi, and Amlan Chakrabarti. "Feature Selection: A Practitioner View." (2014).

[32]Olutayo, V. A., and A. A. Eludire. "Traffic Accident Analysis Using Decision Trees and Neural Networks." International Journal of Information Technology and Computer Science (IJITCS) 6.2 (2014): 22.