Surface color perception and illuminant cues in dynamic three-dimensional scenes

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One of the most impressive aspects of human vision is the stability of surface colors under very different lighting conditions. The visual system is remarkably good at separating the color of the illumination from the actual colors of surfaces and engineers are not currently able to build a camera that can match human performance. Most previous research has focused on very simple stimuli consisting of a small number of flat colored surfaces arranged on a flat panel under diffuse (uniform) lighting. The proposed research makes use of recent advances in computer graphics to create physically-accurate virtual scenes filled with objects at many different depths and orientations illuminated by realistic daylight illuminants mimicking the effects of sun, sky and cloud cover. The major challenge for the visual system in such scenes is that illumination is rarely uniform. In the research proposed here, the investigator will analyze how the visual system uses simple "cues" about illumination conditions such as surface shading and highlights to estimate stable surface colors. The use of eye tracking technology allows the experimenter to monitor where the observer looks in gathering information about the conditions of scene illumination. The proposed research represents the first use of eye-tracking technology to study surface color perception.The human ability to assign stable surface colors and stable shapes to objects is an extraordinary achievement. This research will lead to a better understanding of how the brain adaptively makes use of sources of information about depth and color in scenes which, in turn, will provide a better understanding of how the brain reacts to injury, disease and even normal aging. Moreover, research on how the brain uses cues to scene layout and lighting can inform the production of artificial visual systems that duplicate biological function. Subtle changes in surface color accompany many disease states in plant and animal and such systems would have evident applications in remote sensing, from monitoring crops to early detection of illness.