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Image statistics and the perception of surface qualities

Author

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  • Isamu Motoyoshi

    (Human and Information Science Lab, NTT Communication Science Labs, Nippon Telegraph and Telephone Corporation, 3-1 Morinosato-Wakamiya, Atsugi, 243-0198, Japan)

  • Shin'ya Nishida

    (Human and Information Science Lab, NTT Communication Science Labs, Nippon Telegraph and Telephone Corporation, 3-1 Morinosato-Wakamiya, Atsugi, 243-0198, Japan)

  • Lavanya Sharan

    (Massachusetts Institute of Technology, 43 Vassar Street, 46-4115, Cambridge, Massachusetts 02139, USA)

  • Edward H. Adelson

    (Massachusetts Institute of Technology, 43 Vassar Street, 46-4115, Cambridge, Massachusetts 02139, USA)

Abstract

Texture is skin deep We can easily tell whether an object is made of pewter or plaster, or whether wood is unfinished or polished, by observing the light/dark and shiny/matt qualities of the surface. This involves a chicken-and-egg problem. To infer reflective qualities of a three-dimensional surface, we need detailed information about the shape and the illumination, but inferring each of these components requires knowledge of the others. Motoyoshi et al. have cracked the code, and in doing so they give pointers that could be useful to developers of robotic vision systems and to computer animators creating realistic scenes. The key is some simple image statistics: our perception of glossiness is determined by the amount of positive skew in the distribution of the luminance values in an image.

Suggested Citation

  • Isamu Motoyoshi & Shin'ya Nishida & Lavanya Sharan & Edward H. Adelson, 2007. "Image statistics and the perception of surface qualities," Nature, Nature, vol. 447(7141), pages 206-209, May.
    • Handle: RePEc:nat:nature:v:447:y:2007:i:7141:d:10.1038_nature05724
    DOI: 10.1038/nature05724
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    Citations

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    Cited by:

    1. Eva Marckhgott & Bernadette Kamleitner, 2019. "Matte matters: when matte packaging increases perceptions of food naturalness," Marketing Letters, Springer, vol. 30(2), pages 167-178, June.
    2. Tony Vladusich & Marcel P Lucassen & Frans W Cornelissen, 2007. "Brightness and Darkness as Perceptual Dimensions," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-10, October.
    3. Hideyoshi Yanagisawa & Kenji Takatsuji, 2017. "Expectation effect of perceptual experience in sensory modality transitions: modeling with information theory," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1635-1644, October.
    4. Jun Liu & Junyu Dong & Xiaoxu Cai & Lin Qi & Mike Chantler, 2015. "Visual Perception of Procedural Textures: Identifying Perceptual Dimensions and Predicting Generation Models," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-22, June.
    5. Masataka Sawayama & Shin'ya Nishida, 2018. "Material and shape perception based on two types of intensity gradient information," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-40, April.
    6. Isamu Motoyoshi, 2020. "Adaptive comparison matrix: An efficient method for psychological scaling of large stimulus sets," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    7. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.

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