Does Contour Classification Precede Contour Grouping in Perception of Partially Visible Figures?

Abstract

When a figure is only partially visible and its contours represent a small fraction of total image contours (as when there is much background clutter), a fast contour classification mechanism may filter non-figure contours in order to restrict the size of the input to subsequent contour grouping mechanisms. The results of two psychophysical experiments suggest that the human visual system can classify figure from non-figure contours on the basis of a difference in some contour property (eg length, orientation, curvature, etc). While certain contour properties (eg orientation, curvature) require only local analysis for classification, other contour properties (eg length) may require more global analysis of the retinal image. We constructed a pyramid-based computational model based on these observations and performed two simulations of experiment 1: one simulation with classification enabled and the other simulation with classification disabled. The classification-based simulation gave the superior account of human performance in experiment 1. When a figure is partially visible, with few contours relative to the number of non-figure contours, contour classification followed by contour grouping can be more efficient than contour grouping alone, owing to smaller input to grouping mechanisms.