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, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. 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. 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.

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High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multiscale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.

Our world consists not only of objects and scenes but also of materials of various kinds. Being able to recognize the materials that surround us (e.g., plastic, glass, concrete) is important for humans as well as for computer vision systems. Unfortunately, materials have received little attention in the visual recognition literature, and very few computer vision systems have been designed specifically to recognize materials. In this paper, we present a system for recognizing material categories from single images. We propose a set of low and mid-level image features that are based on studies of human material recognition, and we combine these features using an SVM classifier. Our system outperforms a state-of-the-art system [Varma and Zisserman, 2009] on a challenging database of real-world material categories [Sharan et al., 2009]. When the performance of our system is compared directly to that of human observers, humans outperform our system quite easily. However, when we account for the local nature of our image features and the surface properties they measure (e.g., color, texture, local shape), our system rivals human performance. We suggest that future progress in material recognition will come from: (1) a deeper understanding of the role of non-local surface properties (e.g., extended highlights, object identity); and (2) efforts to model such non-local surface properties in images.