Abstract
It has been generally accepted that the retina is the first level that plays critical role in the function of color constancy in human visual system. In this paper we propose a computational model which imitates the neural mechanisms of color information processing in retina. In this model we first compute a simple statistics of reflectance about the real scene by simulating bipolar cells. Then, the ganglion cells (e.g., the midget cells) receive both the computational statistics of reflectance of the scene from bipolar cells and the response from horizontal cells as well as amacrine cells. Subsequently, the ganglion cells provide a stable output independent of the color of illuminant. We tested our model on a commonly used large-scale linear image dataset, and the results demonstrate that our model provides better color constancy performance than those widely accepted color constancy models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Conway, B.R., Livingstone, M.S.: A different point of hue. Proceedings of the National Academy of Sciences of the United States of America 102(31), 10761 (2005)
Gegenfurtner, K.R.: Cortical mechanisms of colour vision. Nature Reviews Neuroscience 4(7), 563–572 (2003)
Conway, B.R., Chatterjee, S., Field, G.D., Horwitz, G.D., Johnson, E.N., Koida, K., Mancuso, K.: Advances in color science: from retina to behavior. The Journal of Neuroscience 30(45), 14955–14963 (2010)
Schein, S., Desimone, R.: Spectral properties of V4 neurons in the macaque. The Journal of Neuroscience 10(10), 3369–3389 (1990)
Kolb, H.: How the retina works. American Scientist 91(1), 28–35 (2003)
Land, E.H., McCann, J.J.: Lightness and retinex theory. Journal of the Optical society of America 61(1), 1–11 (1971)
Spitzer, H., Semo, S.: Color constancy: a biological model and its application for still and video images. Pattern Recognition 35(8), 1645–1659 (2002)
Huang, C.H., Lin, C.T.: Bio-inspired computer fovea model based on hexagonal-type cellular neural network. IEEE Transactions on Circuits and Systems I: Regular Papers 54(1), 35–47 (2007)
Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310(1), 1–26 (1980)
Finlayson, G., Hordley, S., Tastl, I.: Gamut constrained illuminant estimation. International Journal of Computer Vision 67(1), 93–109 (2006)
Van De Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)
Barnard, K., Adviser-Funt, B.: Practical colour constancy. Simon Fraser University (1999)
Von Kries, J.: Influence of adaptation on the effects produced by luminous stimuli, pp. 109–119. MIT Press (1970)
Hordley, S.D., Finlayson, G.D.: Reevaluation of color constancy algorithm performance. JOSA A 23(5), 1008–1020 (2006)
Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited, pp. 1–8. IEEE (2008)
Funt., L.S.a.B.V.: Re-processed version of the Gehler color constancy database of 568 images, http://www.cs.sfu.ca/~colour/data/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, S., Li, Y. (2012). A Retinal Mechanism Based Color Constancy Model. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)