Variability in constancy of the perceived surface reflectance across different illumination statistics

Abstract

In contrast to the classical findings of lightness constancy, recent psychophysical studies show the strong dependency of the perceived reflectance of a surface on the structure of the natural illumination. The present study examined this inconstancy for systematic variations in the light field and an image-based explanation for it. Observers matched the specular and diffuse reflectance of a three-dimensional object in a complex scene under a fixed light field to that in the scene under different light fields with variable mean, contrast, and gamma. For the both specular and diffuse components, the matched reflectance was relatively constant against changes in the mean illuminance but varied extensively with changes in the contrast and gamma of the light field. We found that the matching data were well predicted by the similarity of the subband histograms of the images. The results support the notion that early spatial filtering can provide a unified account of both the constancy in the perceived surface reflectance against mean illuminance and the inconstancy for higher-order illumination statistics.

Investigating perceptual qualities of static surface appearance using real materials and displayed images

“…Most of these studies have focused on the visual estimation of the specific properties of materials (Anderson, 2011;Thompson, Fleming, Creem-Regehr, & Stefanucci, 2011;Zaidi, 2011), such as glossiness (Fleming, Dror, & Adelson, 2003;Motoyoshi & Matoba, 2012;Nishida & Shinya, 1998), translucency (Fleming & Bülthoff, 2005 Maloney, 2011;Motoyoshi, 2010), or roughness (Padilla, Drbohlav, Green, Spence, & Chantler, 2008;Pont & Koenderink, 2005;Pont & Koenderink, 2008). Taken together, these findings support the general idea that the human visual system can estimate the properties of materials from relatively low-level vision features.…”

Accuracy and speed of material categorization in real-world images

“…By using such controlled stimuli, a number of facts about reflectance perception have been established. It is known that the perceived reflectance of a surface depends not only on its physical reflectance properties (Gilchrist & Jacobsen, 1984;Pellacini, Ferwerda, & Greenberg, 2000;Xiao & Brainard, 2008), but also on its surface geometry (Bloj, Kersten, & Hurlbert, 1999;Boyaci, Maloney, & Hersh, 2003;VanGorp, Laurijssen, & Dutre, 2007;Ho, Landy, & Maloney, 2008), the illumination conditions (Fleming, Dror, & Adelson, 2003;Maloney & Yang, 2003;Gerhard & Maloney, 2010;Olkonnen & Brainard, 2010;Brainard & Maloney, 2011;Motoyoshi & Matoba, 2011), the surrounding surfaces (Gilchrist et al, 1999;Doerschner, Maloney, & Boyaci, 2010;Radonjić, Todorović, & Gilchrist, 2010), the presence of specular highlights (Beck & Prazdny, 1981;Todd, Norman, & Mingolla, 2004;Berzhanskaya, Swaminathan, Beck, & Mingolla, 2005; and specular lowlights , the presence of binocular disparity and surface motion (Hartung & Kersten, 2002;Sakano & Ando, 2010;Wendt, Faul, Ekroll, & Mausfeld, 2010;Doerschner et al, 2011;Kerrigan & Adams, 2013), image-based statistics (Nishida & Shinya, 1998;Motoyoshi, Nishida, Sharan, & Adelson, 2007;Sharan, Li, Motoyoshi, Nishida, & Adelson, 2008), and object identity (Olkkonen, Hansen, & Gegenfurtner, 2008). Recent work has extended this understanding of reflectance perception to include translucent materials (Fleming &amp...…”

A unified account of gloss and lightness perception in terms of gamut relativity

“…According to theoretical approaches based on the classical computational view of vision [4][5][6], the visual system computes lightness and gloss at each image location as a function of spatial image gradients correlated to diffuse and specular reflectance components, respectively [7][8][9][10][11]. The modularity inherent in this notion has spawned a surfeit of perceptually and computationally formulated theories of gloss and lightness perception [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]-each with a limited explanatory rangerather than a unified, predictive theory.…”

“…Two recent approaches to understanding gloss and lightness perception have focused on the putative computational roles of "image statistics" [7,8,[22][23][24]26,27] and "layered surface representations" [4,[28][29][30][31][32][33], respectively. On the one hand, Adelson and co-workers [22][23][24]26,27] have attempted to sidestep the formidable problem of how the visual system is able to correctly discriminate [34] spatial image gradients associated with variations in diffuse and specular reflectance from those associated with variations in illumination intensity [17,18], surface geometry/relief [4,28,[30][31][32][33]35,36] and physical transmittance [18,37].…”

“…On the one hand, Adelson and co-workers [22][23][24]26,27] have attempted to sidestep the formidable problem of how the visual system is able to correctly discriminate [34] spatial image gradients associated with variations in diffuse and specular reflectance from those associated with variations in illumination intensity [17,18], surface geometry/relief [4,28,[30][31][32][33]35,36] and physical transmittance [18,37]. These authors have posited that the visual system employs simple statistically based heuristics to compute gloss and lightness.…”