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A fundamental problem in image analysis is to understand the nature of the computations and mechanisms that provide information about the material properties of surfaces. Information about a surface's 3D shape, optics, illumination field, and atmospheric conditions are conflated in the image, which must somehow be disentangled to derive the properties of surfaces. It was recently suggested that the visual system exploits some simple image statistics-histogram or sub-band skew-to infer the lightness and gloss of surfaces (I. Motoyoshi, S. Nishida, L. Sharan, & E. H. Adelson, 2007). Here, we show that the correlations Motoyoshi et al. (2007) observed between skew, lightness, and gloss only arose because of the limited space of surface geometries, reflectance properties, and illumination fields they evaluated. We argue that the lightness effects they reported were a statistical artifact of equating the means of images with skewed histograms, and that the perception of gloss requires an analysis of the consistency between the estimate of a surface's 3D shape and the positions and orientations of highlights on a surface. We argue that the derivation of surface and material properties requires a photo-geometric analysis, and that purely photometric statistics such as skew fail to capture any diagnostic information about surfaces because they are devoid of the structural information needed to distinguish different types of surface attributes.
Anderson et al. (Thu,) studied this question.