Eden E. Sterk¹, Madelyn G. Arena², Christopher DiMattina²; ¹Florida Southwestern State College, ²Florida Gulf Coast University

To correctly parse the visual scene, one must detect edges and determine their underlying cause. Previous work 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 exhibited strong correlation with human performance on the same task, we 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. Observers were trained to correctly identify the cause of natural image patches (occlusion, shadow, uniform surface). These same observers then classified 5 sets of synthetic images defined by varying sharpness, luminance, and texture cues. These three cues interacted strongly to determine categorization. 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. 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.

Acknowledgements: C.D. is funded by NIH grant R15-EY032732-01