Cybernetics and Systems Analysis

, Volume 55, Issue 5, pp 701–713 | Cite as

Principles of Organization of the Human Eye Retina and Their Use in Computer Vision Systems

  • V. P. BoyunEmail author
  • L. O. Voznenko
  • I. F. Malkush


This paper provides a summary of principles of organization of the human eye retina. The following principles are considered: locality during interacting neurons, ring organization of receptive fields with on- and off-centers (center-surround organization), specialization of neuron layers, organization of feedbacks, adaptation to lighting and contrast levels, and data volume reduction in a video stream. It is shown that the perfect organization of the human retina being used as a prototype allows to significantly improve technical characteristics of computer vision systems. The results of this research were used in creating a family of intelligent video cameras and a number of systems based on them and also in constructing specialized neural networks for primary information processing directly on a sensor matrix.


retina rod cell cone cell horizontal cell bipolar cell amacrine cell ganglion cell on-center off-center neural network video sensor control of parameters of reading information intelligent video camera multilayer matrix structure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Harvey Richard Schiffman, Sensation and Perception: An Integrated Approach [Russian translation], Piter, St. Petersburg (2003).Google Scholar
  2. 2.
    V. P. Boyun, “Human visual analyzer as a prototype for designing a family of problem-oriented machine vision systems,” in: Proc. Intern. Sci. and Techn. Conf. “Artificial Intelligence. Intelligent Systems (AI-2000),” Vol. 1, Donetsk (2010), pp. 21–26.Google Scholar
  3. 3.
    C.-H. Huang and C.-T. Lin, “A bio-inspired computer fovea model based on hexagonal-type cellular neural networks,” IEEE Trans. on Circuits and Systems I: Regular Papers, Vol. 54, Iss. 1, 35–47 (2007).CrossRefGoogle Scholar
  4. 4.
    S. Shah and M. D. Levine, “Visual information processing in primate cone pathways. I. A model,” IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, Vol. 26, No. 2, 259–274 (1996).CrossRefGoogle Scholar
  5. 5.
    A. Benoit, A. Caplier, B. Durette, and J. Herault, “Using human visual system modeling for bio-inspired low level image processing,” Computer Vision and Image Understanding, Vol. 114, Iss. 7, 758–773 (2010).CrossRefGoogle Scholar
  6. 6.
    D. Anderson, Cognitive Psychology [Russian translation], Piter, St. Petersburg (2002).Google Scholar
  7. 7.
    H. Kolb, “How the retina works: Much of the construction of an image takes place in the retina itself through the use of specialized neural circuits,” American Scientist, Vol. 91, No. 1, 28–35 (2003).CrossRefGoogle Scholar
  8. 8.
    Yu. E. Shelepin, V. M. Bondarko, and M. V. Danilova, “Foveola construction and visual system pyramidal organization model,” Sensory Systems, Vol. 9, No. 1, 87–97 (1995).Google Scholar
  9. 9.
    P. J. Burt, “Smart sensing within a pyramid vision machine,” Proc. IEEE, Vol. 76, Iss. 8, 1006–1015 (1988).CrossRefGoogle Scholar
  10. 10.
    V. P. Boyun, “Intelligent selective perception of visual information. Informational aspects,” Artificial Intelligence, No. 3, 16–24 (2011).Google Scholar
  11. 11.
    V. Boyun, “Intelligent selective perception of visual information in vision systems,” in: Proc. 6th IEEE Intern. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application. (IDAACS’2011) (Prague, Czech Republic, 15–17 September 2011), Vol. 1 (2011), pp. 412–416.Google Scholar
  12. 12.
    V. P. Boyun, Dynamic Information Theory. Fundamentals and Applications [in Russian], V. M. Glushkov Institute of Cybernetics (2001).Google Scholar
  13. 13.
    V. Boyun, “Directions of development of intelligent real time video systems,” Application and Theory of Computer Technology, [S. l], Vol. 2, No. 3, 48–66 (2017). Scholar
  14. 14.
    O. G. Rudenko and E. V. Bodyansky, Artificial Neural Networks [in Russian], SMITH Company, Kharkiv (2005).Google Scholar
  15. 15.
    R. Gonzalez and R. Woods, Digital Image Processing [Russian translation], Technosphere, Moscow (2005).Google Scholar
  16. 16.
    V. Boyun, “Bioinspired approaches to the selection and processing of video information,” in: Proc. IEEE Second Intern. Conf. on Data StreamMining & Processing (DSMP) (2018), pp. 498–502.
  17. 17.
    V. P. Boyun, Device for Determining the Location and Parameters of Image Objects, UA Patent No. 76597, BI No. 6 (2013).Google Scholar
  18. 18.
    V. P. Boyun, Sensor Device for Determination of Location and Center of Gravity of an Object, UA Patent No. 106292, BI No.12 (2014).Google Scholar
  19. 19.
    V. P. Boyun, Sensor Device for Determining the Location and Moments of Inertia of an Object in an Image, UA Patent No. 106301, BI No. 15 (2014).Google Scholar
  20. 20.
    V. P. Boyun, Sensor Matrix with Image Processing, UA Patent No. 109335, BI No. 6, (2015).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.V. M. Glushkov Institute of CyberneticsNational Academy of Sciences of UkraineKyivUkraine

Personalised recommendations