AUTOMATIC GAIN CONTROL NETWORKS FOR MULTIDIMENTIONAL VISUAL ADAPTATION

S. Furman, Y. Y. Zeevi

Abstract

Processing and analysis of images are implemented in the multidimensional space of visual information representation. This space includes the well investigated dimensions of intensity, color and spatio-temporal frequency. There are, however, additional less investigated dimensions such as curvature, size and depth (for example - from binocular disparity). Along these dimensions, the human visual system (HVS) enhances and emphasizes important image attributes by adaptation and nonlinear filtering. It is interesting and possible to emulate the visual system processing of images along these dimensions, in order to achieve intelligent image processing and computer vision. Sparsely connected, recurrent adaptive sensory neural network (NN), incorporating non-linear interactions in the feedback loops, are presented. Such generic NN exhibit Automatic Gain Control (AGC) model of processing along the visual dimensions. The results are compared with those of psychophysical experiments exhibiting good reproduction of visual illusions.

References

  1. Abbott, L. F. et al., 1997. Synaptic Depression and Cortical Gain Control. Science, 275(5297), 221-224.
  2. Blakemore, C. & Campbell, F. W., 1969. On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. The Journal of Physiology, 203(1), 237-260.1.
  3. Bradshaw, M. F., Parton, A. D. & Glennerster, A., 2000. The task-dependent use of binocular disparity and motion parallax information. Vision Research, 40(27), 3725-3734.
  4. Bradshaw, M. F. & Rogers, B. J., 1996. The Interaction of Binocular Disparity and Motion Parallax in the Computation of Depth. Vision Research, 36(21), 3457-3468.
  5. Bruckstein, A. M. & Zeevi, Y.Y., 1979. Analysis of "integrate-to-threshold" neural coding schemes. Biological Cybernetics, 34(2), 63-79.
  6. Bruno, N. & Cutting, J. E., 1988. Minimodularity and the perception of layout. Journal of Experimental Psychology. General, 117(2), 161-170.
  7. Hubel, D. H. & Wiesel, T. N., 1970. Stereoscopic Vision in Macaque Monkey: Cells sensitive to Binocular Depth in Area 18 of the Macaque Monkey Cortex. Nature, 225, 41-42.
  8. Inui, T. et al., 2000. Neural substrates for depth perception of the Necker cube; a functional magnetic resonance imaging study in human subjects. Neuroscience Letters, 282(3), 145-148.
  9. Jagadish, H. V., 1990. Linear clustering of objects with multiple attributes. In Proceedings of the 1990 ACM SIGMOD international conference on Management of data, 332-342.
  10. Kimmel, R., Malladi, R. & Sochen, N., 2000. Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images. International Journal of Computer Vision, 39(2), 111- 129.
  11. Koenderink, J. J. & Doom, A. J .V., 1982. The shape of smooth objects and the way contours end. Perception, 11(2), 129 - 137.
  12. Koenderink, J. J. & Doorn, A. J. V., 1987. Representation of local geometry in the visual system. Biological Cybernetics, 55(6), 367-375.
  13. Krauskopf, J. & Mollon, J. D., 1971. The independence of the temporal integration properties of individual chromatic mechanisms in the human eye. The Journal of Physiology, 219(3), 611-623.
  14. Lu, Z. & Sperling, G., 1996. Contrast gain control in firstand second-order motion perception. Journal of the Optical Society of America A, 13(12), 2305-2318.
  15. Parent, P. & Zucker, S.W., 1989. Trace Inference, Curvature Consistency, and Curve Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(8), 823-839.
  16. Ratliff, F., 1965. Mach bands: Quantitative studies on neural networks in the retina. Holden-Day inc.
  17. Richards, W., Dawson, B. & Whittington, D., 1986. Encoding contour shape by curvature extrema. Journal of the Optical Society of America A, 3(9), 1483-1491.
  18. Riggs, L. A., 1973. Curvature as a Feature of Pattern Vision. Science, 181(4104), 1070-1072.
  19. Roberts, B., Harris, M.G. & Yates, T.A., 2005. The roles of inducer size and distance in the Ebbinghaus illusion (Titchener circles). Perception, 34(7), 847 - 856.
  20. Rogers, B. J. & Graham, M., 1979. Motion parallax as an independent cue for depth perception. Perception, 8(2), 125 - 134.
  21. Schwartz, O. & Simoncelli, E. P., 2001. Natural signal statistics and sensory gain control. Nat Neurosci, 4(8), 819-825.
  22. Shefer, M., 1979. AGC models for retinal signal processing. M.Sc. Thesis. Technion.
  23. Sochen, N. & Zeevi, Y. Y., 1998. images as manifolds embedded in a spatial feature non euclidean space. IEEE ICIP, 166-170.
  24. Stromeyer, C. F. & Riggs, L.A., 1974. Curvature Detectors in Human Vision? Science, 184(4142), 1199-1201.
  25. Sutherland, N. S., 1968. Outlines of a Theory of Visual Pattern Recognition in Animals and Man. Proceedings of the Royal Society of London. Series B, Biological Sciences, 171(1024), 297-317.
  26. Treisman, A. M. & Gormican, S., 1988. Feature analysis in early vision: Evidence from search asymmetries. Psychological Review, 95(1), 15-48.
  27. Wainwright, M. J., 1999. Visual adaptation as optimal information transmission. Vision Research, 39(23), 3960-3974.
  28. Weltsch-Cohen, Y., 2002. AGC models for signal processing in the primary visual cortex. M.Sc. Thesis. Technion.
  29. Wolfe, J. M. et al., 2003. Changing your mind: On the contributions of top-down and bottom-up guidance in visual search for feature singletons. Journal of Experimental Psychology: Human Perception and Performance, 29(2), 483-502.
  30. Zeevi, Y. Y., Ginosar, R. & Hilsenrath, O., 1995. United States Patent: 5420637 - Dynamic image representation system.
  31. Zeevi, Y. Y. & Kronauer, E. R., 1985. Reorganization and diversification of signals in vision. EEE transactions on systems, man, and cybernetics, 15(1), 91-101.
  32. Zeevi, Y. Y. & Mangoubi, S.S., 1978. Noise suppression in photoreceptors and its relevance to incremental intensity thresholds. Journal of the Optical Society of America, 68 (12), 1772-1776.
  33. Zucker, S. W. et al., 1988. The Organization Of Curve Detection: Coarse Tangent Fields And Fine Spline Coverings. In Computer Vision., Second International Conference, 568-577.
Download


Paper Citation


in Harvard Style

Furman S. and Zeevi Y. (2010). AUTOMATIC GAIN CONTROL NETWORKS FOR MULTIDIMENTIONAL VISUAL ADAPTATION . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 163-175. DOI: 10.5220/0003061901630175


in Bibtex Style

@conference{icnc10,
author={S. Furman and Y. Y. Zeevi},
title={AUTOMATIC GAIN CONTROL NETWORKS FOR MULTIDIMENTIONAL VISUAL ADAPTATION},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={163-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003061901630175},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - AUTOMATIC GAIN CONTROL NETWORKS FOR MULTIDIMENTIONAL VISUAL ADAPTATION
SN - 978-989-8425-32-4
AU - Furman S.
AU - Zeevi Y.
PY - 2010
SP - 163
EP - 175
DO - 10.5220/0003061901630175