Image statistics and the perception of surface qualities

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 surfaces1,2,3, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention4,5,6,7. 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 them8,9,10,11. 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.

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Figure 1: These two synthetic images of Michelangelo’s St Matthew sculpture have the same mean luminance.
Figure 2: Perceived lightness and glossiness may be based on the skewness of the luminance histograms.
Figure 3: A proposed neural mechanism for encoding the sub-band skewness by early visual units.
Figure 4: After-effects of perceived lightness and glossiness.

References

  1. 1

    Land, E. H. & McCann, J. J. Lightness and retinex theory. J. Opt. Soc. Am. 61, 1–11 (1971)

    ADS  CAS  Article  Google Scholar 

  2. 2

    Gilchrist, A. et al. An anchoring theory of lightness perception. Psych. Rev. 106, 795–834 (1999)

    CAS  Article  Google Scholar 

  3. 3

    Brainard, D. H. Color constancy in the nearly natural image. 2. Achromatic loci. J. Opt. Soc. Am. A 15, 307–325 (1998)

    ADS  CAS  Article  Google Scholar 

  4. 4

    Dana, K. J. et al. Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18, 1–34 (1999)

    Article  Google Scholar 

  5. 5

    Todd, J. T., Norman, J. F. & Mingolla, E. Lightness constancy in the presence of specular highlights. Psych. Sci. 15, 33–39 (2004)

    Article  Google Scholar 

  6. 6

    Pont, S. C. & Koenderink, J. J. Bidirectional texture contrast function. Int. J. Comp. Vis. 62, 17–34 (2005)

    Article  Google Scholar 

  7. 7

    Robilotto, R. & Zaidi, Q. Lightness identification of patterned three-dimensional, real objects. J. Vis. 6, 18–36 (2006)

    Article  Google Scholar 

  8. 8

    Nishida, S. & Shinya, M. Use of image-based information in judgments of surface-reflectance properties. J. Opt. Soc. Am. A 15, 2951–2965 (1998)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Fleming, R. W., Dror, R. O. & Adelson, E. H. Real-world illumination and the perception of surface reflectance properties. J. Vis. 3, 347–368 (2003)

    Article  Google Scholar 

  10. 10

    Dror, R. O., Willsky, A. S. & Adelson, E. H. Statistical characterization of real-world illumination. J. Vis. 4, 821–837 (2004)

    Article  Google Scholar 

  11. 11

    Fleming, R. W. & Bülthoff, H. H. Low-level image cues in the perception of translucent materials. ACM Trans. Appl. Percept. 2, 346–382 (2005)

    Article  Google Scholar 

  12. 12

    Levoy, M. et al. The Digital Michelangelo Project. 〈http://graphics.stanford.edu/projects/mich〉 (2004)

  13. 13

    Nicodemus, F. Directional reflectance and emissivity of an opaque surface. Appl. Opt. 4, 767–773 (1965)

    ADS  Article  Google Scholar 

  14. 14

    MacGillivray, H. L. Skewness and asymmetry: measures and orderings. Ann. Stat. 14, 994–1011 (1986)

    MathSciNet  Article  Google Scholar 

  15. 15

    Heeger, D. J. Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. J. Neurophysiol. 70, 1885–1898 (1993)

    CAS  Article  Google Scholar 

  16. 16

    Schiller, P. H., Finlay, B. L. & Volman, S. F. Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. J. Neurophysiol. 39, 1288–1319 (1976)

    CAS  Article  Google Scholar 

  17. 17

    Baizer, J. S., Robinson, D. L. & Dow, B. M. Visual responses of area 18 neurons in awake, behaving monkey. J. Neurophysiol. 40, 1024–1037 (1977)

    CAS  Article  Google Scholar 

  18. 18

    Shipp, S. & Zeki, S. The functional organization of area V2, I: specialization across stripes and layers. Vis. Neurosci. 19, 187–210 (2002)

    Article  Google Scholar 

  19. 19

    Kagan, I., Gur, M. & Snodderly, D. M. Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings. J. Neurophysiol. 88, 2257–2274 (2002)

    Article  Google Scholar 

  20. 20

    Mata, M. L. & Ringach, D. L. Spatial overlap of ON and OFF subregions and its relation to response modulation ratio in macaque primary visual cortex. J. Neurophysiol. 93, 919–928 (2005)

    Article  Google Scholar 

  21. 21

    Malik, J. & Perona, P. Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A 5, 923–932 (1990)

    ADS  Article  Google Scholar 

  22. 22

    Chubb, C., Landy, M. S. & Econopouly, J. A visual mechanism tuned to black. Vision Res. 44, 3223–3232 (2004)

    Article  Google Scholar 

  23. 23

    Olshausen, B. A. & Field, D. J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)

    ADS  CAS  Article  Google Scholar 

  24. 24

    Simoncelli, E. P. & Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001)

    CAS  Article  Google Scholar 

  25. 25

    Kingdom, F. A. A., Hayes, A. & Field, D. J. Sensitivity to contrast histogram differences in synthetic wavelet-textures. Vision Res. 41, 585–598 (2001)

    CAS  Article  Google Scholar 

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Acknowledgements

We thank Y. Li for discussions. L.S. and E.H.A. were supported by NTT and by a grant from the National Science Foundation to E.H.A.

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Correspondence to Isamu Motoyoshi.

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Supplementary information

Supplementary Information 1

This file contains Supplementary Figure 1 which is a schematic of the main finding of the study, Supplementary Methods, Supplementary Video Legend which describes how to play the aftereffects demo movies, the Supplementary Data which is divided into four sections titled A through D, and the Supplementary Discussion. Supplementary Data A along with Supplementary Figures 2–4 describe the results for perceived lightness and glossiness for 42 natural surface images. Supplementary Data B and Supplementary Figures 5–8 compare the effects of various image statistics on lightness and glossiness perception. Supplementary Data C and Supplementary Figures 9 and 10 describe the effect of randomizing spatial structures in the image of a surface. Supplementary Data D and Supplementary Figures 11 and 12 contrast the effects of luminance skewness and subband skewness on the perceived lightness and glossiness. Supplementary Discussion and Supplementary Figures 13 and 14 describe the details of the proposed skewness detection mechanism. (PDF 1227 kb)

Supplementary Video 1

This file contains Supplementary Video 1 ‘stucco1’. For further information about the movie please see page 6 of the main Supplementary Information document. (MOV 1348 kb)

Supplementary Video 2

This file contains Supplementary Video 2 ‘stucco2’. For further information about the movie please see page 6 of the main Supplementary Information document. (MOV 1348 kb)

Supplementary Video 3

This file contains Supplementary Video 3 ‘texture1’. For further information about the movie please see page 6 of the main Supplementary Information document. (MOV 1319 kb)

Supplementary Video 4

This file contains Supplementary Video 4 ‘texture 2’. For further information about the movie please see page 6 of the main Supplementary Information document. (MOV 1320 kb)

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Motoyoshi, I., Nishida, S., Sharan, L. et al. Image statistics and the perception of surface qualities. Nature 447, 206–209 (2007). https://doi.org/10.1038/nature05724

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