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A Biologically Motivated Double-Opponency Approach to Illumination Invariance

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7726))

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Abstract

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.

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References

  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. 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. Chalupa, L.M., Werner, J.S.: The visual neurosciences. The MIT Press (2004)

    Google Scholar 

  4. von Kries, J.: Òbeitrag zur physiologie der gesichtsempfinding. Arch. Anat. Physiol. 2, 505–524 (1878)

    Google Scholar 

  5. Worthey, J.A., Brill, M.H.: Heuristic analysis of von kries color constancy. J. Optical Society of America A 3, 1708–1712 (1986)

    Article  Google Scholar 

  6. Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Optical Society of America A 61, 1–11 (1971)

    Article  Google Scholar 

  7. Brainard, D., Wandell, B.: Analysis of the retinex theory of color vision. J. Optical Society of America A 3, 1651–1661 (1986)

    Article  Google Scholar 

  8. D’Zmura, M., Lennie, P.: Mechanisms of color constancy. J. Optical Society of America A 3, 1662–1672 (1986)

    Article  Google Scholar 

  9. Hurlbert, A.: Formal connections between lightness algorithms. J. Optical Society of America A 3, 1684–1693 (1986)

    Article  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  15. Ratnasingam, S., McGinnity, T.M.: A chromaticity space for illuminant invariant recognition. IEEE Transaction in Image Processing 21, 3612–3623 (2012)

    Article  MathSciNet  Google Scholar 

  16. Foster, D.H.: Color constancy. Vision Research 51, 674–700 (2011), doi:10.1016/j.visres.2010.09.006

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  18. Dacey, D.: Parallel pathways for spectral coding in primate retina. Annu. Rev. Neurosci. 23, 743–775 (2000)

    Article  Google Scholar 

  19. Komatsu, H.: Mechanisms of central color vision. Curr. Opin. Neurobiol. 8, 503–508 (1998)

    Article  Google Scholar 

  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. Horn, B.K.P., Brooks, M.J.: The variational approach to shape from shading. CVGIP 33, 174–208 (1986)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  24. Stevens, S.: Psychophysics: introduction to its perceptual, neural, and social prospects. Transaction Publishers (2007)

    Google Scholar 

  25. Finlayson, G.D., Hordley, S.D.: Colour constancy at a pixel. J. Optical Society of America A 18, 253–264 (2001)

    Article  Google Scholar 

  26. Kamermans, M., Spekreijse, H.: Spectral behavior of cone-driven horizontal cells in teleost retina. Prog. Ret. Eye Res. 14, 313–360 (1995)

    Article  Google Scholar 

  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. Courtney, S.M., Finkel, L.H., Buchsbaum, G.: simulation of retinal and cortical contributions to color constancy. Vision Res. 35, 413–434 (1995)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  31. Nocedal, J., Wright, S.: Numerical Optimization. Springer (2000)

    Google Scholar 

  32. Arnold, S.E.J., Savolainen, V., Chittka, L.: The floral reflectance spectra database. In: Nature Proceedings (2008), http://dx.doi.org/10.1038/npre.2008.1846.1

  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. Hernandez-Andres, J., Lee Jr., R.L., Romero, J.: Color and luminance asymmetries in the clear sky. J. Appl. Opt. 42, 458–464 (2003)

    Article  Google Scholar 

  35. Finlayson, G.D., Hordley, S.D.: Colour constancy at a pixel. J. Optical Society of America A 18, 253–264 (2001)

    Article  Google Scholar 

  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. Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310, 337–350 (1980)

    Article  Google Scholar 

  38. van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16, 2207–2214 (2007)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. NICTA, Locked Bag 8001, Canberra, ACT, 2601, Australia

    Sivalogeswaran Ratnasingam & Antonio Robles-Kelly

  2. Research School of Eng., ANU, Canberra, ACT, 0200, Australia

    Sivalogeswaran Ratnasingam & Antonio Robles-Kelly

Authors
  1. Sivalogeswaran Ratnasingam
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  2. Antonio Robles-Kelly
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Ratnasingam, S., Robles-Kelly, A. (2013). A Biologically Motivated Double-Opponency Approach to Illumination Invariance. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_23

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  • DOI: https://doi.org/10.1007/978-3-642-37431-9_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37430-2

  • Online ISBN: 978-3-642-37431-9

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