Reconciling the statistics of spectral reflectance and colour


Autoři: Lewis D. Griffin aff001
Působiště autorů: Computer Science, UCL, London, United Kingdom aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223069

Souhrn

The spectral reflectance function of a surface specifies the fraction of the illumination reflected by it at each wavelength. Jointly with the illumination spectral density, this function determines the apparent colour of the surface. Models for the distribution of spectral reflectance functions in the natural environment are considered. The realism of the models is assessed in terms of the individual reflectance functions they generate, and in terms of the overall distribution of colours which they give rise to. Both realism assessments are made in comparison to empirical datasets. Previously described models (PCA- and fourier-based) of reflectance function statistics are evaluated, as are improved versions; and also a novel model, which synthesizes reflectance functions as a sum of sigmoid functions. Key model features for realism are identified. The new sigmoid-sum model is shown to be the most realistic, generating reflectance functions that are hard to distinguish from real ones, and accounting for the majority of colours found in natural images with the exception of an abundance of vegetation green and sky blue.

Klíčová slova:

Distance measurement – Eyes – Leaves – Light – Machine learning – Normal distribution – principal component analysis – Sensory perception


Zdroje

1. Popper K. Three worlds: Ann Arbor,: University of Michigan.; 1979.

2. Zeki S, Nash J. Inner vision: An exploration of art and the brain: Oxford university press Oxford; 1999.

3. Louwerse MM. Embodied relations are encoded in language. Psychonomic Bulletin & Review. 2008;15(4):838–44.

4. Fechner G. Elements of psychophysics: Holt, Rinehart & Winston New York; 1965.

5. Koenderink JJ. Solid Shape: MIT Press; 1990.

6. Gegenfurtner KR, Sharpe LT. Color vision: From genes to perception: Cambridge University Press; 2001.

7. Nayar SK, Ikeuchi K, Kanade T. Surface reflection: physical and geometrical perspectives. IEEE Transactions on Pattern Analysis & Machine Intelligence. 1991;(7):611–34.

8. Born M, Wolf E. Principles of optics: electromagnetic theory of propagation, interference and diffraction of light: Elsevier; 2013.

9. Oyster CW. The human eye: structure and function: Sinauer Associates; 1999.

10. Stockman A. Color vision mechanisms: University of Pennsylvania; 2010.

11. Conway BR, Chatterjee S, Field GD, Horwitz GD, Johnson EN, Koida K, et al. Advances in color science: from retina to behavior. The Journal of Neuroscience. 2010;30(45):14955–63. doi: 10.1523/JNEUROSCI.4348-10.2010 21068298

12. Chatterjee S, Callaway EM. Parallel colour-opponent pathways to primary visual cortex. Nature. 2003;426(6967):668. doi: 10.1038/nature02167 14668866

13. Mylonas D, MacDonald L, Wuerger S, editors. Towards an online color naming model. Color and Imaging Conference; 2010: Society for Imaging Science and Technology.

14. Kelley K, Judd D. The ISCC-NBS methods of designating colors and adictionary of color names.[US] Natl. Bur Standards Cir. 1955;553.

15. Berlin B, Kay P. Basic Color Terms: their Universality and Evolution. Berkeley: University of California Press; 1969.

16. Griffin LD. Similarity of psychological and physical colour space shown by symmetry analysis. Color Research and Application. 2001;26(2):151–7. WOS:000167092000005.

17. Foster DH. Does colour constancy exist? Trends in cognitive sciences. 2003;7(10):439–43. 14550490

18. Finlayson GD, Drew MS, Funt BV. Spectral sharpening: sensor transformations for improved color constancy. JOSA A. 1994;11(5):1553–63. doi: 10.1364/josaa.11.001553 8006721

19. Maloney LT. Evaluation of linear models of surface spectral reflectance with small numbers of parameters. JOSA A. 1986;3(10):1673–83.

20. Fairman HS, Brill MH. The principal components of reflectances. Color Research & Application. 2004;29(2):104–10.

21. Koenderink JJ. The prior statistics of object colors. Journal of the Optical Society of America A, Optics, image science, and vision. 2010;27(2):206–17. Epub 2010/02/04. doi: 10.1364/JOSAA.27.000206 20126232.

22. Clark RN, Swayze GA, Wise R, Livo KE, Hoefen TM, Kokaly RF, et al. USGS digital spectral library splib06a. US Geological Survey Reston, VA; 2007.

23. Kohonen O, Parkkinen J, Jääskeläinen T. Databases for spectral color science. Color Research & Application. 2006;31(5):381–90.

24. Webster MA, Mollon J. Adaptation and the color statistics of natural images. Vision research. 1997;37(23):3283–98. doi: 10.1016/s0042-6989(97)00125-9 9425544

25. Nascimento SM, Ferreira FP, Foster DH. Statistics of spatial cone-excitation ratios in natural scenes. JOSA A. 2002;19(8):1484–90. doi: 10.1364/josaa.19.001484 12152688

26. Webster MA, Mizokami Y, Webster SM. Seasonal variations in the color statistics of natural images. Network: Computation in neural systems. 2007;18(3):213–33.

27. Koenderink J, van Doorn A. Colors of the Sublunar. i-Perception. 2017;8(5):2041669517733484. doi: 10.1177/2041669517733484 28989697

28. Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A, Van Wicklin C, et al. THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. bioRxiv. 2019:545954.

29. Griffin LD. Optimality of the basic colour categories for classification. Journal of the Royal Society: Interface. 2006;3(6):71–85. doi: 10.1098/rsif.2005.0076 WOS:000235712600007. 16849219

30. Breiman L. Random forests. Machine learning. 2001;45(1):5–32.

31. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology). 2005;67(2):301–20.

32. Ohta N, Robertson A. Colorimetry: fundamentals and applications: John Wiley & Sons; 2006.

33. Stockman A, Sharpe LT. The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype. Vision Research. 2000;40(13):1711–37. doi: 10.1016/s0042-6989(00)00021-3 WOS:000087362500008. 10814758

34. Anderson M, Motta R, Chandrasekar S, Stokes M, editors. Proposal for a standard default color space for the internet—srgb. Color and imaging conference; 1996: Society for Imaging Science and Technology.

35. Koenderink JJ. Color Atlas Theory. Journal Of The Optical Society Of America A-Optics Image Science And Vision. 1987;4(7):1314–21. doi: 10.1364/josaa.4.001314 WOS:A1987J043300021.

36. Foss CE, Nickerson D, Granville WC. Analysis of the Ostwald color system. JOSA. 1944;34(7):361–8.

37. Griffin LD. Partitive mixing of images: a tool for investigating pictorial perception. Journal of the Optical Society of America A-Optics Image Science and Vision. 1999;16(12):2825–35. WOS:000088951800002.

38. Coxeter HSM. Introduction to geometry. 1961.

39. Griffin LD, Mylonas D. Categorical colour geometry. PloS one. 2019;14(5):e0216296. doi: 10.1371/journal.pone.0216296 31075109

40. Abdi H, Williams LJ. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010;2(4):433–59.

41. Vrhel MJ, Gershon R, Iwan LS. Measurement and analysis of object reflectance spectra. Color Research & Application. 1994;19(1):4–9.

42. Olkkonen M, Hansen T, Gegenfurtner KR. Categorical color constancy for simulated surfaces. Journal of vision. 2009;9(12):6–. doi: 10.1167/9.12.6 20053097


Článek vyšel v časopise

PLOS One


2019 Číslo 11