August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
The Dark Secrets of Dirty Concavities
Author Affiliations
  • Roland Fleming
    Experimental Psychology, University of Giessen
  • Steven Cholewiak
    Experimental Psychology, University of Giessen
Journal of Vision August 2014, Vol.14, 1317. doi:10.1167/14.10.1317
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      Roland Fleming, Steven Cholewiak; The Dark Secrets of Dirty Concavities. Journal of Vision 2014;14(10):1317. doi: 10.1167/14.10.1317.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Cracks, crevices and other surface concavities are typically dark places where both dirt and shadows tend to get trapped. By contrast, convex features are exposed to light and often get buffed a lighter or more glossy shade through contact with other surfaces. This means that in many cases, for complex surface geometries, shading and pigmentation are spatially correlated with one another, with dark concavities that are dimly illuminated and lighter convexities, which are more brightly shaded. How does the visual system distinguish between pigmentation and shadows when the two are spatially correlated? We performed a statistical analysis of complex rough surfaces under illumination conditions that varied parametrically from highly directional to highly diffuse in order to characterise the relationships between shading, illumination and shape. Whereas classical shape from shading analyses relate image intensities to surface orientations and depths, here, we find that intensity information also carries important additional cues to surface curvature. By shifting the phase of dark portions of the image relative to the surface geometry, we show that the visual system uses these relationships between curvatures and intensities to distinguish between shadows and pigmentation. Interestingly, we also find that the visual system is remarkably good at separating pigmentation and shadows even when they are highly correlated with one another, as long as the illumination conditions provide subtle local image orientation cues to distinguish the two. Together, these findings provide key novel constraints on computational models of human shape from shading and lightness perception.

Meeting abstract presented at VSS 2014

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