A computational model of periodic-pattern-selective cells

  • P. Kruizinga
  • N. Petkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


A computational model of so-called grating cells is proposed. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond strongly to bar gratings of a given orientation and periodicity but very weakly or not at all to single bars. This non-linear behavior is quite different from the spatial frequency filtering behavior exhibited by the other types of orientation selective cells. It is incorporated in the proposed model by using an AND-like non-linearity to combine the responses of simple cells and compute the activities of so-called grating subunits which are subsequently summed up. The parameters of the model are adjusted to reproduce the results measured by neurophysiologists with different visual stimuli. The proposed computational model of a grating cell is used to compute the collective activation of sets of such cells, referred to as cortical images, induced by natural visual stimuli. On the basis of the results of such simulations we speculate about the possible role of grating cells in the visual system and demonstrate the usefulness of grating cell operators for some computer vision tasks, such as automatic face recognition and document processing.


Grating cells visual cortex computational model texture analysis face recognition document processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J.G. Daugman: “Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters”, Journal of the Optical Society of America A, Vol.2 (1985) No. 7, pp.1160–1169.Google Scholar
  2. [2]
    F. Heitger, L. Rosenthaler, R. von der Heydt, E. Peterhans, O. Kubier: “Simulation of neural contour mechanisms: from simple to end-stopped cells”, Vision Research, Vol 23 (1992) No. 5, pp.963–981.Google Scholar
  3. [3]
    D. Hubel and T. Wiesel: “Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex”, J. Physiol. (London), Vol. 160 (1962), pp.106–154.Google Scholar
  4. [4]
    D.H. Hubel: “Explorations of the primary visual cortex, 1955–1978 ” (1981 Nobel Prize lecture), Nature, Vol. 299 (1982) pp.515–524.PubMedGoogle Scholar
  5. [5]
    S.W. Kuffler: “Discharge patterns and functional organization of mammalian retina”, Journal of Neurophysiology, Vol.16 (1953) pp.37–68PubMedGoogle Scholar
  6. [6]
    N. Petkov, P. Kruizinga and T. Lourens: ”Biologically Motivated Approach to Face Recognition”, Proc. International Workshop on Artificial Neural Networks, June 9–11, 1993, Sitges (Barcelona), Spain (Berlin: Springer Verlag, 1993) pp.68–77Google Scholar
  7. [7]
    N. Petkov, T. Lourens and P. Kruizinga: “Lateral inhibition in cortical filters”, Proc. of Int. Conf. on Digital Signal Processing and Int. Conf. on Computer Applications to Engineering Systems, July 14–16, 1993, Nicosia, Cyprus, pp.122–129.Google Scholar
  8. [8]
    N. Petkov, P. Kruizinga and T. Lourens: “Orientation competition in cortical filters — An application to face recognition”, Computing Science in The Netherlands 1993, Nov. 9–10, 1993, Utrecht (Stichting Mathematisch Centrum: Amsterdam, 1993) pp.285–296.Google Scholar
  9. [9]
    T. Lourens, N. Petkov, and P. Kruizinga. “Large scale natural vision simulations”, Future Generation Computer Systems, Issue: High Performance Computing and Networking (HPCN), 10:351–358, June 1994.Google Scholar
  10. [10]
    N. Petkov: Biologically motivated image classification system, in ed. Ph. Laplante and A. Stoyenko Real-Time Imaging (Academic Press, 1995, in print) 31 pagesGoogle Scholar
  11. [11]
    H. Spitzer and S. Hockstein: “A complex-cell receptive field model”, Journal of Neurophysiology, Vol.53 (1985), pp.1266–1286.PubMedGoogle Scholar
  12. [12]
    R. von der Heydt, E. Peterhans and M.R. Dürsteler: “Periodic-pattern-selective cells in monkey visual cortex”, The journal of neuroscience, April 1992, 12(4), pp.1416–1434PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • P. Kruizinga
    • 1
  • N. Petkov
    • 1
  1. 1.Dept. of Computing ScienceUniversity of GroningenAV GroningenThe Netherlands

Personalised recommendations