Local cues enable classification of image patches as surfaces, object boundaries, or illumination changes.

Abstract

To correctly parse the visual scene, one must detect edges and determine their underlying cause. Previous work has demonstrated that image-computable neural networks trained to differentiate natural shadow and occlusion edges exhibited sensitivity to boundary sharpness and texture differences. Although these models showed a strong correlation with human performance on an edge classification task, this previous study did not directly investigate whether humans actually make use of boundary sharpness and texture cues when classifying edges as shadows or occlusions. Here we directly investigated this using synthetic image patch stimuli formed by quilting together two different natural textures, allowing us to parametrically manipulate boundary sharpness, texture modulation, and luminance modulation. In a series of initial "training" experiments, observers were trained to correctly identify the cause of natural image patches taken from one of three categories (occlusion, shadow, uniform texture). In a subsequent series of "test" experiments, these same observers then classified 5 sets of synthetic boundary images defined by varying boundary sharpness, luminance modulation, and texture modulation cues using a set of novel parametric stimuli. These three visual cues exhibited strong interactions to determine categorization probabilities. For sharp edges, increasing luminance modulation made it less likely the patch would be classified as a texture and more likely it would be classified as an occlusion, whereas for blurred edges, increasing luminance modulation made it more likely the patch would be classified as a shadow. Boundary sharpness had a profound effect, so that in the presence of luminance modulation increasing sharpness decreased the likelihood of classification as a shadow and increased the likelihood of classification as an occlusion. Texture modulation had little effect on categorization, except in the case of a sharp boundary with zero luminance modulation. Results were consistent across all 5 stimulus sets, showing these effects are not due to the idiosyncrasies of the particular texture pairs. Human performance was found to be well explained by a simple linear multinomial logistic regression model defined on luminance, texture and sharpness cues, with slightly improved performance for a more complicated nonlinear model taking multiplicative parameter combinations into account. Our results demonstrate that human observers make use of the same cues as our previous machine learning models when detecting edges and determining their cause, helping us to better understand the neural and perceptual mechanisms of scene parsing.