A computational model of periodic-pattern-selective cells
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.
KeywordsGrating cells visual cortex computational model texture analysis face recognition document processing
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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