Abstract
A new computational analysis is described for estimating the 3D shapes of curved surfaces with contour textures. This model assumes that contours on a surface are stacked in depth so that the depth interval between any two points is optically specified by the number of contours by which they are separated. Whenever this assumption is violated, the model makes specific predictions about how the apparent shape of a surface should be distorted. Two psychophysical experiments were performed in an effort to compare the model predictions with the perceptual judgments of human observers. Stimuli consisted of sinusoidally corrugated surfaces with contours that were oriented in different directions. In Experiment 1 images of textured surfaces were presented together with a set of red and yellow dots that could be moved along a single horizontal scan line with a handheld mouse. Observers were instructed to mark each local depth minimum on the scan line with a red dot and each local depth maximum with a yellow dot. In Experiment 2 horizontal scan lines on images were marked by a row of five to eight equally spaced red dots. An identical row of dots was presented against a blank background on a separate monitor, each of which could be moved perpendicularly with a handheld mouse. Observers were instructed to adjust the dots on the second monitor in order to match the apparent surface profile in depth along the designated scan line. The results of both experiments revealed that observers' shape judgments are close to veridical when surface contours are stacked in depth, but that contour patterns that violate this constraint produce systematic distortions in the apparent shapes of surfaces that are quite consistent with our proposed model.
This research was supported by a grant from NSF (BCS-0546107).