1. Human and Information Science Lab, NTT Communication Science Labs, Nippon Telegraph and Telephone Corporation, 3-1 Morinosato-Wakamiya, Atsugi, 243-0198, Japan
2. Department of Brain and Cognitive Sciences and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 43 Vassar Street, 46-4115, Cambridge, Massachusetts 02139, USA

Correspondence to: Isamu Motoyoshi¹ Correspondence and requests for materials should be addressed to I.M. (Email: motoyosi@apollo3.brl.ntt.co.jp).

Abstract

The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces^{1, 2, 3}, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention^{4, 5, 6, 7}. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them^{8, 9, 10, 11}. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.

1. Human and Information Science Lab, NTT Communication Science Labs, Nippon Telegraph and Telephone Corporation, 3-1 Morinosato-Wakamiya, Atsugi, 243-0198, Japan
2. Department of Brain and Cognitive Sciences and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 43 Vassar Street, 46-4115, Cambridge, Massachusetts 02139, USA

Correspondence to: Isamu Motoyoshi¹ Correspondence and requests for materials should be addressed to I.M. (Email: motoyosi@apollo3.brl.ntt.co.jp).

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