A Biologically Motivated Double-Opponency Approach to Illumination Invariance

  • Sivalogeswaran Ratnasingam
  • Antonio Robles-Kelly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


In this paper we propose a biologically inspired computational model based upon the human visual pathway in order to achieve a feature pair that is robust to changes in scene illumination variation. Here, we draw inspiration from the V4 area in the visual cortex and utilise an approach based upon both, the colour opponency and the spatially opponent centre surround receptive field mechanisms present in the human visual system. We do this making use of an optimisation setting which yields the optimal synaptic strength of the centre-surround neurons based on the colour discrimination for the double-opponent feature pair. This approach greatly reduces the effects of the illuminant in terms of discrimination of perceptually similar colours. We illustrate the utility of our approach for purposes of recognising perceptually similar colours, colour-based object recognition and skin detection under widely varying illumination conditions using bench marked data sets. We also compared our results to those yielded by a number of alternatives.


Human Visual System Synaptic Weight Colour Constancy Skin Detection Retinex Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR, pp. 2169–2178 (2006)Google Scholar
  2. 2.
    Ebner, M.: How does the brain arrive at a color constant descriptor? In: Proceedings of the 2nd International Conference on Advances in Brain Vision and Artificial Intelligence (2007)Google Scholar
  3. 3.
    Chalupa, L.M., Werner, J.S.: The visual neurosciences. The MIT Press (2004)Google Scholar
  4. 4.
    von Kries, J.: Òbeitrag zur physiologie der gesichtsempfinding. Arch. Anat. Physiol. 2, 505–524 (1878)Google Scholar
  5. 5.
    Worthey, J.A., Brill, M.H.: Heuristic analysis of von kries color constancy. J. Optical Society of America A 3, 1708–1712 (1986)CrossRefGoogle Scholar
  6. 6.
    Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Optical Society of America A 61, 1–11 (1971)CrossRefGoogle Scholar
  7. 7.
    Brainard, D., Wandell, B.: Analysis of the retinex theory of color vision. J. Optical Society of America A 3, 1651–1661 (1986)CrossRefGoogle Scholar
  8. 8.
    D’Zmura, M., Lennie, P.: Mechanisms of color constancy. J. Optical Society of America A 3, 1662–1672 (1986)CrossRefGoogle Scholar
  9. 9.
    Hurlbert, A.: Formal connections between lightness algorithms. J. Optical Society of America A 3, 1684–1693 (1986)CrossRefGoogle Scholar
  10. 10.
    Pinto, N., Stone, Z.Z.T., Cox, D.: Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In: Workshop on Biologically Consistent Vision (2011)Google Scholar
  11. 11.
    Semo, S., Spitzer, H.: Color constancy: a biological model and its application for still and video images. In: The 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, pp. 198–201 (2000)Google Scholar
  12. 12.
    Finlayson, G.D., Drew, M.S.: 4-sensor camera calibration for image representation invariant to shading shadows lighting and specularities. In: ICCV 2001, pp. 473–480 (2001)Google Scholar
  13. 13.
    Ratnasingam, S., Collins, S.: An Algorithm to Determine the Chromaticity Under Non-uniform Illuminant. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 244–253. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Ratnasingam, S., Collins, S.: Study of the photodetector characteristics of a camera for colour constancy in natural scene. J. Optical Society of America A 27, 286–294 (2010)CrossRefGoogle Scholar
  15. 15.
    Ratnasingam, S., McGinnity, T.M.: A chromaticity space for illuminant invariant recognition. IEEE Transaction in Image Processing 21, 3612–3623 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Foster, D.H.: Color constancy. Vision Research 51, 674–700 (2011), doi:10.1016/j.visres.2010.09.006CrossRefGoogle Scholar
  17. 17.
    Herault, J.: A model of colour processing in the retina of vertebrates: From photoreceptors to colour opposition and colour constancy phenomena. Neurocomputing 12, 113–129 (1996)zbMATHCrossRefGoogle Scholar
  18. 18.
    Dacey, D.: Parallel pathways for spectral coding in primate retina. Annu. Rev. Neurosci. 23, 743–775 (2000)CrossRefGoogle Scholar
  19. 19.
    Komatsu, H.: Mechanisms of central color vision. Curr. Opin. Neurobiol. 8, 503–508 (1998)CrossRefGoogle Scholar
  20. 20.
    Ferster, D.: Spatially opponent excitation and inhibition in simple cells of the cat visual cortex. The Journal of Neuroscience 8, 1172–1180 (1988)Google Scholar
  21. 21.
    Horn, B.K.P., Brooks, M.J.: The variational approach to shape from shading. CVGIP 33, 174–208 (1986)zbMATHGoogle Scholar
  22. 22.
    Kimmel, R., Elad, M., Shaked, D., Keshet, R., Sobel, I.: A variational framework for retinex. International Journal of Computer Vision 52, 1393–1411 (2003)CrossRefGoogle Scholar
  23. 23.
    Finlayson, G.D., Schaefer, G.: Solving for colour constancy using a constrained dichromatic reflection model. International Journal of Computer Vision 42, 127–144 (2001)zbMATHCrossRefGoogle Scholar
  24. 24.
    Stevens, S.: Psychophysics: introduction to its perceptual, neural, and social prospects. Transaction Publishers (2007)Google Scholar
  25. 25.
    Finlayson, G.D., Hordley, S.D.: Colour constancy at a pixel. J. Optical Society of America A 18, 253–264 (2001)CrossRefGoogle Scholar
  26. 26.
    Kamermans, M., Spekreijse, H.: Spectral behavior of cone-driven horizontal cells in teleost retina. Prog. Ret. Eye Res. 14, 313–360 (1995)CrossRefGoogle Scholar
  27. 27.
    Ts’o, D., Gilbert, C.D.: The organization of chromatic and spatial interactions in the primate striate cortex. J. Neurosci. 8, 1712–1727 (1988)Google Scholar
  28. 28.
    Courtney, S.M., Finkel, L.H., Buchsbaum, G.: simulation of retinal and cortical contributions to color constancy. Vision Res. 35, 413–434 (1995)CrossRefGoogle Scholar
  29. 29.
    Moore, A., Allman, J., Goodmann, R.M.: A real-time neural system for color constancy. IEEE Transactions on Neural Networks 2, 237–247 (1991)CrossRefGoogle Scholar
  30. 30.
    Stiles, W.S., Burch, J.M.: Interim report to the Commission Internationale de l’Éclairage Zurich, 1955, on the National Physical Laboratory’s investigation of colour-matching. Optica Acta 2, 168–181 (1955)CrossRefGoogle Scholar
  31. 31.
    Nocedal, J., Wright, S.: Numerical Optimization. Springer (2000)Google Scholar
  32. 32.
    Arnold, S.E.J., Savolainen, V., Chittka, L.: The floral reflectance spectra database. In: Nature Proceedings (2008),
  33. 33.
    Abrardo, A., Cappellini, V., Cappellini, M., Mecocci, A.: Art-works colour calibration using the vasari scanner. In: Color Imaging Conference: Color Science, Systems and Applications, pp. 94–97 (1996)Google Scholar
  34. 34.
    Hernandez-Andres, J., Lee Jr., R.L., Romero, J.: Color and luminance asymmetries in the clear sky. J. Appl. Opt. 42, 458–464 (2003)CrossRefGoogle Scholar
  35. 35.
    Finlayson, G.D., Hordley, S.D.: Colour constancy at a pixel. J. Optical Society of America A 18, 253–264 (2001)CrossRefGoogle Scholar
  36. 36.
    Funt, B., Barnard, K., Martin, L.: Is Machine Colour Constancy Good Enough? In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 445–459. Springer, Heidelberg (1998)Google Scholar
  37. 37.
    Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310, 337–350 (1980)CrossRefGoogle Scholar
  38. 38.
    van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16, 2207–2214 (2007)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Hwang, C.L., Lu, K.D.: The segmentation of different skin colors using the combination of graph cuts and probability neural network. In: The 11th International Conference on Artificial Neural Networks Conference on Advances in Computational Intelligence, pp. 8–10 (2011)Google Scholar
  40. 40.
    Shoyaib, M., Abdullah-Al-Wadud, M., Chae, O.: A skin detection approach based on the dempster-shafer theory of evidence. International Journal of Approximate Reasoning 53, 636–659 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sivalogeswaran Ratnasingam
    • 1
    • 2
  • Antonio Robles-Kelly
    • 1
    • 2
  1. 1.NICTACanberraAustralia
  2. 2.Research School of Eng.ANUCanberraAustralia

Personalised recommendations