Human Perception of Depth and Surface Lightness

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

Though it might surprise the average person, human vision has not yet been explained scientifically. For example, we do not yet have enough information about vision to design a robot that can actually see. No robot or computer exists that can determine the size or even the color of an object in its field of view. Although the perception of size and color is easy for human vision, we do not know what kind of "software" the brain uses to do this. The open questions addressed in this project include how the human visual system detects lightness: the black, white, or gray shade of objects in the visual field, and especially how this detection depends on the perceived distance of that surface. A second issue in this research is methodological: Researchers testing lightness perception using paper of varying lightness have found different results from those found by researchers using computer monitors. Obviously, a theory of visual phenomena would not expect such differences. With support of the National Science Foundation, Dr. Alan Gilchrist will examine the nature of the software of lightness perception. In a series of experiments, Dr. Gilchrist will explore the relations between surface lightness, lightness context, illumination, and surface distance. All the experiments will be conducted in two ways: (1) using actual 3D displays made from paper, and (2) using simulated displays created with CRT images in a mirror stereoscope. This work will clarify a series of nagging and controversial questions concerning the nature of the relationship between lightness perception and depth perception (two of the most important visual dimensions) and between lightness perception and perception of the illumination level. The resulting understanding will have a number of impacts. It will help guide neuroscientists in what to look for in the brain, both in the study of vision itself, and in the study of cognitive processes more generally. The work also will benefit machine vision, by laying the groundwork for successful machine-vision algorithms, thereby enhancing our technical capabilities. The project will also provide a needed service to the field of vision by advancing the integration of two bodies of research on lightness, those using traditional paper and light displays, and those using stereo CRT images.