“Human vision and computer graphics” by Montalvo

  • ©Fanya S. Montalvo




    Human vision and computer graphics



    Is one picture really worth a thousand words? Why do cleverly designed graphic displays make visual information stand out more clearly with strikingly greater impact than numbers buried in pages of computer printout? Graphic output devices shift the burden of integrating information generated by computers onto the human vision system: the sensory channel with the highest capacity for distributed parallel processing. The system consists of hundreds of successive two-dimensional arrays of millions of interconnected parallel computers. Perception seems instantaneous because we are not conscious of the massive amounts of computation that occur. What we consciously “see at a glance” is already a highly structured, synthesized, and summarized version of the actual light intensity mosaic that enters the retina. We will demonstrate some results of the visual structuring that occurs in the human visual system, show why some features stand out instantaneously and others do not, and explain why knowledge of the human input device is crucial to the design of effective computer output devices and displays.


    1. Abramov, I. Further analysis of the responses of LGN cells. J.Opt.Soc.Am. 58, 574-579 (1968).
    2. Bajcsy, R. A computational structure for color perception. Moore School of EE Tech. Report, University of Pennsylvania, Philadelphia (1975).
    3. Barlow, H.B., Blakemore, C. & Pettigrew, J.D. The neural mechanism of binocular depth discrimination. J.Physiol. 193, 327-342 (1967).
    4. Barlow, H.B., Hill, R.M. & Levick, W.R. Retinal ganglion cells responding selectively to direction and speed of image motion in the rabbit. J.Physiol. 173, 377-407 (1964).
    5. Berry, R.N. Quantitative relations among vernier, real depth, and stereoscopic depth acuities. J.Exp.Psychol. 38, 708-721 (1948).
    6. Blakemore, C. & Campbell, F.W. On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. J.Physiol. 203, 237-260 (1969).
    7. Boynton, R.M. & Kaiser, P.K. Vision: the additivity law made to work for heterochromatic photometry with bipartite fields. Science 161, 366-368 (1968).
    8. Cornsweet, T.N. Visual Perception. New York: Academic Press, 1970.
    9. Crow, F.C. The aliasing problem in computer-synthesized shaded images. CACM 20, 799-805 (1977).
    10. DeValois, R.L. Analysis and coding of color vision in the primate visual system. Cold Spring Harbor Symp.Quant.Biol. 30, 567-579 (1965).
    11. DeValois, R.L., Abramov, I. & Jacobs, G.H. Analysis of response patterns of LGN cells. J.Opt.Soc.Am. 56, 966-977 (1966).
    12. DeValois, R.L. & Pease, P.L. Contours and contrast: responses of monkey lateral geniculate cells to luminance and color figures. Science 171, 694-696 (1971).
    13. Gregory, R.L. Eye and Brain. New York: McGraw-Hill, 1966.
    14. Joblove, G.H. & Greenberg, D. Color spaces for computer graphics. Proc. SIGGRAPH-78, Atlanta GA, 20-25 (August 1978).
    15. Hamerley, J.R. & Springer, R.M. Perception of raggedness of edge images. Op.Soc.Am.Meeting, San Francisco CA (November 1978).
    16. Harmon, L.D. & Julesz, B. Masking in visual recognition: effects of two-dimensional filtered noise. Science 180, 1194-1196 (1973).
    17. Hubel, D.H. & Wiesel, T.N. Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. J.Physiol. 160, 106-154 (1962).
    18. Hurvich, L.M. & Jameson, D. An opponent-process theory of color vision. Psyc.Rev. 64, 384-404 (1957).
    19. Julesz, B. Foundations of Cyclopean Vision. Chicago: Univ. of Chicago Press, 1971.
    20. Julesz, B. Experiments in the visual perception of texture. Sci.Am. 232, 34-43 (1975).
    21. Julesz, B., Gilbert, E.N., Shepp, L.A. & Frisch, H.L. Inability of humans to discriminate between visual textures that agree in second-order statistics: revisited. Perception 2, 391-405 (1973).
    22. Mansfield, J.R.W. Neural basis of orientation perception in primate vision. Science 186, 1133-1135 (1974).
    23. McCollough, C. Color adaptation of edge-detectors in the human visual system. Science 149, 1115-1116 (1965).
    24. Montalvo, F.S. Aftereffects, adaptation, and plasticity: a neural model for tunable feature space. PhD thesis, Comp. & Info. Sci. Dept. Tech. Report 76-4, University of Massachusetts, Amherst (September 1976).
    25. Over, R. Comparison of normalization theory and neural enhancement explanation of negative aftereffects. Psyc.Bulletin 75, 225-243 (1971).
    26. Ratliff, F. Mach Bands. San Francisco: Holden-Day, 1965.
    27. Ratliff, F. & Hartline, H.K. The responses of Limulus optic nerve fibers to patterns of illumination on the retinal mosaic. J.Gen.Physiol. 42, 1241-1255 (1959).
    28. Riggs, L.A. Visual acuity. In C.H. Graham (Ed.) Vision and Visual Perception. New York: Wiley & Sons, 321-349 (1965).
    29. Smith, A.R. Color gamut transform pairs. Proc. SIGGRAPH-78, Atlanta GA, 12-19 (August 1978).
    30. Van Der Horst, G.J.C. Fourier analysis and color discrimination. J.Opt.Soc.Am. 59, 1670-1676 (1969).
    31. Weisstein, N. Metacontrast. In L.M. Hurvich & D. Jameson (Eds.) Visual Psychophysics. Heidelberg: Springer-Verlag, 233-272 (1972).

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