In this paper, we consider the problem of detection of changes and degree estimation of these changes in a dynamically changing binary image. The authors introduce the numerical characteristic degree of change areas in dynamically changing binary images based on the Jaccard similarity coefficient. To calculate this characteristic, the authors developed an original architecture of a twodimensional cellular automaton with the diffusion dynamics. We establish that cellular automaton configurations converge to a stationary configuration. The stationary configuration of a cellular automaton defines the desired characteristics for each area in dynamically changing binary images. The result can be presented as a grayscale image, which greatly facilitates the visual analysis of the dynamics of changes in binary images. The suggested approach can be used to detect and numerically estimate changes in the case when a number of brightness gradation comprises more than two values.
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Kashkin, V.B. and Sukhinin, A.I., Distantsionnoe zondirovanie Zemli iz kosmosa. Tsifrovaya obrabotka izobrazhenii (Remote Sensing of the Earth from Space. Digital Image Processing), Moscow: Logos, 2001.
Preston, K.J., Duff, M.J.B., Levialdi, S., Norgren, P.E., and Toriwaki, J., Basics on cellular logic with some applications in medical image processing, Proc. IEEE, 1979, vol. 67, no. 5, pp. 826–857.
Preston, K. and Duff, M., Modern Cellular Automata. Theory and Applications, Plenum Press, 1984.
Rosin, P.L., Training cellular automata for image processing, Image Anal., Lect. Notes Comput. Sci, 2005, vol. 3540, pp. 195–204.
Toffoli, T. and Margolus, N., Cellular Automata Machines, Cambridge: MIT Press, 1987.
Weickert, J., Theoretical Foundations of Anisotropic Diffusion in Image Processing, Teubner Verlag, Stuttgart, 1998.
Borisenko, G.V. and Denisov, A.M., Nonlinear source in diffusion filtering methods for image processing, Comput. Math. Math. Phys., 2007, vol. 47, no. 10, pp. 1631–1635.
Bandman, O.L., Cellular automata models of spatial dynamics, in Sistemnaya informatika (System Informatics), Novosibirsk, 2006, vol. 10, pp. 59–113.
Korotkin, A.A. and Majorov, V.V., A neural network with diffusive interaction between elements for selecting changes in a dynamic image, Comput. Math. Math. Phys., 2000, vol. 40, pp. 287–292.
Marmanis, H. and Babenko, D., Algorithms of the Intelligent Web. Manning Publications Co., 2009.
Kaneko, K., Theory and Application of Coupled Map Lattices, John Wiley & Sons Ltd, 1993.
Feller, W., An Introduction to Probability Theory and Its Applications, New York: Wiley, 1957, vol. 1, 2d ed.
Original Russian Text © A.A. Korotkin, A.A. Maksimov, 2014, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2014, Vol. 21, No. 4, pp. 64–74.
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Korotkin, A.A., Maksimov, A.A. Cellular-local algorithm for localizing and estimating of changes in binary images. Aut. Control Comp. Sci. 50, 453–459 (2016). https://doi.org/10.3103/S0146411616070129
- image comparison
- cellular automaton