September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Visual perception of surface wetness
Author Affiliations
  • Masataka Sawayama
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, JAPAN
  • Shin'ya Nishida
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, JAPAN
Journal of Vision September 2015, Vol.15, 937. doi:
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      Masataka Sawayama, Shin'ya Nishida; Visual perception of surface wetness. Journal of Vision 2015;15(12):937.

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      © ARVO (1962-2015); The Authors (2016-present)

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We can visually recognize a variety of surface state. Just a quick look is sufficient to find the bath floor is dry, the road in front is slippery, the window glass is frosty, or the ornament is dusty. If a given surface state perception relies on the analysis of diagnostic image features, an effective strategy to reveal those features and the associated visual processing is to find a stimulus transformation that alters the apparent surface state. Here we report an image transformation that makes dry objects look wet. This wet filter consists of two operations: (1) Tone-remapping with an accelerating nonlinear function that renders the intensity histogram positively skewed: (2) color saturation enhancement. In an experimental test, we applied the wet filter to a variety of natural textures of the McGill Calibrated Colour Image Database. The results of a wetness rating experiment showed that the wet-filtered images were perceived as wetter than the original images. In addition, the perceived wetness depended on the variance of hue. The wet-filter was less effective for images with a small variance of hue. Optically, wetting a surface tends to increases the specular reflection. In addition, as the incoming light scatters repeatedly within the surface liquid layer, the light going out from the surface tends to be darker and more saturated. The effects of these optical changes can be simulated by the two wet-filter operations. However, positively skewed luminance histogram and high chromatic saturation may be caused by other factors — for instance, the visual scene may happen to include highly saturated glossy objects. This is presumably why hue variation matters. If the same image transformation simultaneously occurs in many different objects, the brain infers that the change likely has the same cause, such as water shower in the present case.

Meeting abstract presented at VSS 2015


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