Skip to main content
Log in

Cellular-local algorithm for localizing and estimating of changes in binary images

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. 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.

    Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. Preston, K. and Duff, M., Modern Cellular Automata. Theory and Applications, Plenum Press, 1984.

    Book  MATH  Google Scholar 

  4. Rosin, P.L., Training cellular automata for image processing, Image Anal., Lect. Notes Comput. Sci, 2005, vol. 3540, pp. 195–204.

    Article  Google Scholar 

  5. Toffoli, T. and Margolus, N., Cellular Automata Machines, Cambridge: MIT Press, 1987.

    MATH  Google Scholar 

  6. Weickert, J., Theoretical Foundations of Anisotropic Diffusion in Image Processing, Teubner Verlag, Stuttgart, 1998.

    MATH  Google Scholar 

  7. 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.

    Article  MathSciNet  Google Scholar 

  8. Bandman, O.L., Cellular automata models of spatial dynamics, in Sistemnaya informatika (System Informatics), Novosibirsk, 2006, vol. 10, pp. 59–113.

    Google Scholar 

  9. 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.

    MathSciNet  MATH  Google Scholar 

  10. Marmanis, H. and Babenko, D., Algorithms of the Intelligent Web. Manning Publications Co., 2009.

    Google Scholar 

  11. Kaneko, K., Theory and Application of Coupled Map Lattices, John Wiley & Sons Ltd, 1993.

    MATH  Google Scholar 

  12. Feller, W., An Introduction to Probability Theory and Its Applications, New York: Wiley, 1957, vol. 1, 2d ed.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. A. Korotkin.

Additional information

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.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411616070129

Keywords