Abstract
We investigated the detection of shapes (closed contours) in noise as a function of their complexity. As in previous work (VSS2012), we quantified complexity in several ways reflecting alternate shape-generating models. In a contour-based model, complexity is defined as the integrated surprisal along the contour, that is, the summed negative log probability of the sequence of turning angles defining it. Alternatively, in a skeleton-based model of shape, complexity is defined as the surprisal of the shape given its generating skeleton, that is, the negative log probability of the shape's boundary conditioned on an estimated skeleton. The two models have the same basic probabilistic conception but differ in assumed generating model. In VSS2012 we applied these measures to natural shapes (animals and leaves), but these shapes necessarily result in an irregular and sparse sampling of the shape space. Here we present a series of more controlled experiments in which we manipulated the structure of the target shape in order to distinguish the potentially separate effects of contour complexity and different components of skeleton-based shape complexity, such as the log prior and log likelihood. Subjects detected closed contours embedded in background noise (2IFC task). We parametrically varied several shape factors, including the number of parts in the shape, and the variability of the contour around its internal skeleton (hence the amplitude of contour noise). The number of parts influences the prior probability of the skeleton, while the variability influences the complexity under the contour model as well as the likelihood under the skeletal model. Both factors show significant influences on shape detectability, providing evidence for both contour-based and region-based representational formats. The results shed light on basic processes of perceptual organization, including object segmentation (extracting whole shapes from cluttered scenes) and shape representation.
Meeting abstract presented at VSS 2013