The perception of convex and concave surfaces under natural lighting conditions
Year: 2010
Type: article
Abstract: Much of the recent research on the visual perception of shape from shading has employed computer generated images, which often do not include several basic components of shading that are common in natural scenes, such as cast shadows, indirect reflections and specular highlights. The present research was designed to investigate the effects of these components on observers' perceptions of convex and concave bumps. In the simulated scenes, these bumps were positioned on an inside wall of a room that was illuminated by a square area light in the center of its ceiling. Images were created from a point of observation in the center of the opposite wall. The rendering model used a radiosity algorithm to calculate the indirect reflections within the room, and a ray-tracing algorithm to calculate the cast shadows and specular highlights. Each of these effects could be turned on or off independently, and they were presented to observers in all possible combinations. The simulated bumps had three possible depths for both the concave and convex surfaces, and their images were presented in both upright and inverted orientations. Observers judged the shape of each bump by adjusting a curve on the monitor display screen to match its apparent profile in depth. The results revealed large individual differences in the overall pattern of responses. For some observers there was an inversion of perceived relief when images were presented up-side down, though this effect was generally eliminated when cast shadows were present. A majority of observers had a strong bias to interpret the displays as convex, and the magnitude of perceived depth was significantly larger for the convex surfaces. In general, the highlights and indirect reflections had little effect on performance.
Source: Journal of Vision
DOI: 10.1167/2.7.86
Authors: B. Liu, J. T. Todd
Institution: The Ohio State University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W1995769660