Models and Hardware Implementation of Methods of Pre-processing Images Based on the Cellular Automata
DOI:
https://doi.org/10.14738/aivp.25.561Keywords:
Cellular automata, image, contour, zoom, filling inside area.Abstract
The paper deals with the organization and construction of cellular automata for the implementation of the basic operations of the pre-processing images. The methods of edge detection, zoom, filling inside area of images and also selection of objects are considered. The analysis of the impact of different forms of the neighboring cells for the effective execution of operations is carried. Programs that simulate the operation of CA are developed. Computer models of the main elements in CAD Active-HDL have been obtained by modeling the structure of the CA. The obtained models have passed the test and their analysis showed high reliability of operation. This allows us to implement them in modern CPLD and FPGA hardware. This hardware is easily reprogrammed under the given structure of CA. Implementing FPGAs allows us to use one chip for realization of the basic functions of the CA. The experimental results showed that the used methods and CA are highly effective. The use of CA allows to describe of the image with high speed highly effective.
References
. Von Neumann, J. Theory of Self-Reproducing Automata: Edited and completed by A. Burks. University of Illinois Press, 1966.
. Belan S., Specialized cellular structures for image contour analysis, Cybernetics and Systems Analysis, 2011. 47(5): p. 695-704.
. Wolfram S., Cellular Automata. Los Alamos Science, 1983. 9: p. 2-21.
. Belan S., and Motornyuk R., Extraction of characteristic features of images with the help of the radon transform and its hardware implementation in terms of cellular automata. Cybernetics and Systems Analysis, 2013. 49(1): p. 7-14.
. Ioannidis K., Andreadis I. Sirakoulis G. Ch. An Edge Preserving Image Resizing Method Based on Cellular Automata. Springer-Verlag, Berlin Heidelberg, ACRI2012, LNCS, 749, 2012. 375-384.
. Bandini, S., Bonomi, A., Vizzari, G. An Analysis of Different Types and Effects of Asynchronicity in Cellular Automata update Schemes. Natural Computing, 2012. 11(2): p. 277-287.
. Kozhemyako V., Belan S., Savaliuk I. (1997). Optoelectronic self – reguletion neural system for treatment of vision information. SPIE Proceedings, Wasington, USA, 1997. 3055, p. 120-126.
. Chen M.J., Huang C.H., Lee W.L. (2005). A fast edge-oriented algorithm for image interpolation. Image and Vision Computing, 2005. (23): p. 791–798.
. Canny J. A computational approach to edge-detection. IEEE Trans. Pattern Anal. Mach. Intell, 1986. (8), p. 679–700.
. Wolfram S. Theory and applications of Cellular Automata. World Scientific, Singapore, 1986.
. Amanatiadis A., Andreadis I., Gasteratos, A. A Log-Polar interpolation applied to image scaling. In: IEEE International Workshop on Imaging Systems and Techniques, Cracovia, Poland, 2007, p. 1–5.
. Cha Y., Kim S. The error-amended sharp edge (EASE) scheme for imaging zooming. IEEE Trans. Image Process, 2007. (16), p. 1496–1505.
. Belan S., Belan N. Temporal-Impulse Description of Complex Image Based on Cellular Automata. Springer-Verlag Berlin Heidelberg. PaCT2013, LNCS.- 2013. 7979. 291-295.
