Color vision and image intensities: When are changes material?
- First Online:
- 103 Downloads
The difficulty in understanding a biological system or its components without some idea of its goals has been emphasized by Marr. In this paper, a preliminary goal for color vision is proposed and analyzed. That goal is to determine where changes of material occur in a scene (using only spectral information). The goal is challenging because the effects of many processes (shadowing, shading from surface orientation changes, highlights, variations in pigment density) are confounded with the effects of material changes in the available image intensities. We show there is a minimal and unique condition, the spectral crosspoint, that rejects instances of these confounding processes. (If plots are made of image intensity versus wavelength from two image regions, and the plots intersect, we say that there is a spectral crosspoint.) An operator is designed to detect crosspoints; it turns out to resemble double-opponent cells described in primate visual cortex.
Unable to display preview. Download preview PDF.
- Dartnall, H.J.A.: The identity and distribution of visual pigments in the animal kingdom, Chap. 18. In: The Eye, Vol. 2. Davson, H. (ed.). New York: Academic Press 1962Google Scholar
- Evans, R.M.: An introduction to color. New York: Wiley 1948Google Scholar
- Horn, B.K.P.: Image intensity understanding. M.I.T. Artificial Intelligence Laboratory Memo 335 (1975)Google Scholar
- Judd, D.B., Wyszecki, G.: Color in business, science, and industry. New York: Wiley 1963Google Scholar
- Krinov, E.L.: Spectral reflectance properties of natural formations. National Research Council of Canada Technical Translation 439, 1971Google Scholar
- Kubelka, P., Munk, F.: Ein Beitrag zur Optik der Farbanstriche. Z. tech. Physik 12 593 (1931)Google Scholar
- Lythgoe, J.N.: The ecology of vision. Oxford: Oxford University Press 1979Google Scholar
- Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. San Francisco: Freeman 1982Google Scholar
- Marr, D., Poggio, T.: From understanding computation to understanding neural circuitry. Neurosci. Res. Prog. Bull. 15, 470–488 (1977)Google Scholar
- Michael, C.R.: Color vision mechanisms in monkey striate cortex: simple cells with dual opponent-color receptive fields. J. Neurophys. 41, 1233–1249 (1978)Google Scholar
- Richards, W.A., Rubin, J.M., Hoffman, D.D.: Equation counting and the interpretation of sensory data. M.I.T. Artificial Intelligence Laboratory Memo 614 (1981), and Perception (1982) (in press)Google Scholar
- Rubin, John M., Richards, W.A.: Toward a property-based representation for spectral information M.I.T. Artificial Intelligence Laboratory Memo 655 (1982)Google Scholar
- Silver, W.: Determining shape and reflectance using multiple images. Masters Thesis, Department of Electrical Engineering and Computer Science, M.I.T., June, 1980Google Scholar
- Wyszecki, G., Stiles, W.S.: Color science: concepts and methods, quantitative data and formulas. New York: Wiley 1967Google Scholar