11 July 2016 Decoding figure-ground occlusions from contours and shading
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Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100110E (2016) https://doi.org/10.1117/12.2242783
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
Abstract
Visual experience of surface properties relies on accurately attributing encoded luminance variations (e.g., edges and contours) to any one of several potential environmental causes. We examined the role of differences in the local shading directions across sharp contours in (i) identifying occlusion boundaries and (ii) perceiving the depth layout of adjacent surfaces. We used graphical rendering to control the orientation of a simulated light source, and hence the shading direction between adjacent surface regions that met at a common edge. We call the difference in shading direction across the edge the delta shading angle. We found that delta-shaded edges looked like occluding boundaries. We also found that the perceived figure-ground organisation of the adjacent surface regions depended on an assumed lighting from above prior. Shaded regions experienced as convex surfaces illuminated from above were perceived as occluding surfaces in the foreground. We computed an image-based measure of delta shading using the difference in local shading direction (the orientation field) and found this model could reliably account for observer judgments of surface occlusion, better than local (in-)coherence in the orientation of isophotes across the edge alone. However, additional information from co-alignment of isophotes relative to the edge is necessary to explain figure-ground distinctions across a broad class of occlusion events. We conclude that both local and global measures of shading direction are needed to explain perceived scene organisation, and material appearance more generally.
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Juno Kim, Juno Kim, Stuart Anstis, Stuart Anstis, } "Decoding figure-ground occlusions from contours and shading", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110E (11 July 2016); doi: 10.1117/12.2242783; https://doi.org/10.1117/12.2242783
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