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Color Vision, Computational Methods for

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Encyclopedia of Computational Neuroscience
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Synonyms

Color computational vision; Computational neuroscience of color

Definition

The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.

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Although the sensation of hue is an invention of our brains, it nevertheless allows us to identify...

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References

  • Barnard K, Funt B (2002) Camera characterization for color research. Color Res Appl 27:153–164

    Google Scholar 

  • Boynton RM (1986) A system of photometry and colorimetry based on cone excitations. Color Res Appl 11:244–252

    Article  Google Scholar 

  • Brainard DH (2004) Color constancy. In: Chalupa LM, Werner JS (eds) The visual neurosciences. MIT Press, Cambridge, MA, pp 948–961

    Google Scholar 

  • Cheung V, Westland S, Connah D, Ripamonti C (2004) A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms. Color Technol 120:19–25

    Article  CAS  Google Scholar 

  • De Valois R (2004) Neural coding of color. In: Werner JS, Chalupa LM (eds) The visual neurosciences. MIT Press, Cambridge, MA, p 1001

    Google Scholar 

  • De Valois RL, De Valois KK (1988) Spatial vision. Oxford University Press, New York

    Google Scholar 

  • Derrington AM, Krauskopf J, Lennie P (1984) Chromatic mechanisms in lateral geniculate-nucleus of macaque. J Physiol 357:241–265

    PubMed  CAS  PubMed Central  Google Scholar 

  • Fairchild MD (1998) Color appearance models. Addison-Wesley, Reading/Harlow

    Google Scholar 

  • Gevers T (2012) Color in computer vision: fundamentals and applications. Wiley, Hoboken

    Book  Google Scholar 

  • Green P, MacDonald L (2002) Colour engineering: achieving device independent colour. Wiley, Chichester

    Google Scholar 

  • Gregory RL (1998) Seeing colours. In: Eye and brain: the psychology of seeing. Oxford University Press, Oxford, pp 121–134

    Google Scholar 

  • Heeger DJ (1992) Normalization of cell responses in cat striate cortex. Vis Neurosci 9:181–197

    Article  PubMed  CAS  Google Scholar 

  • Helmholtz HV (1867) Handbuch f physiologischen Optik. Voss, Leipzig

    Google Scholar 

  • Hering E (1875) Zur Lehre vom Lichtsinne. Sechs Mittheilungen an die Kaiserl. Akademie der Wissenschaften in Wien, 2nd edn. Gerold, Wien

    Google Scholar 

  • Hurlbert A (2003) Colour vision: primary visual cortex shows its influence. Curr Biol 13:R270–R272

    Article  PubMed  CAS  Google Scholar 

  • Judd DB (1951) Report of U.S. secretariat committee on colorimetry and artificial daylight. In: Twelfth session of the CIE. Bureau Central de la CIE, Stockholm, p 11

    Google Scholar 

  • MacLeod DIA, Boynton RM (1979) Chromaticity diagram showing cone excitation by stimuli of equal luminance. J Opt Soc Am 69:1183–1187

    Article  PubMed  CAS  Google Scholar 

  • Murray N, Vanrell M, Otazu X, Parraga CA (2011) Saliency estimation using a non-parametric low-level vision model. In: Computer vision and pattern recognition (CVPR), 2011 I.E. conference on, pp 433–440

    Google Scholar 

  • Otazu X, Parraga CA, Vanrell M (2010) Towards a unified model for chromatic induction. J Vis 10(5):1–24

    Article  Google Scholar 

  • Parraga CA, Troscianko T, Tolhurst DJ (2002) Spatiochromatic properties of natural images and human vision. Curr Biol 12:483–487

    Article  PubMed  CAS  Google Scholar 

  • Parraga CA, Baldrich R, Vanrell M (2010) Accurate mapping of natural scenes radiance to cone activation space: a new image dataset. In: CGIV 2010/MCS’10 – 5th European conference on colour in graphics, imaging, and vision – 12th international symposium on multispectral colour science. Society for Imaging Science and Technology, Joensuu, pp 50–57

    Google Scholar 

  • Poynton CA (2003) Digital video and HDTV: algorithms and interfaces. Morgan Kaufmann, Amsterdam/ Boston

    Google Scholar 

  • Shapley R, Hawken MJ (2011) Color in the cortex: single- and double-opponent cells. Vision Res 51:701–717

    Article  PubMed  PubMed Central  Google Scholar 

  • Singer B, D’Zmura M (1995) Contrast gain control: a bilinear model for chromatic selectivity. J Opt Soc Am A Opt Image Sci Vis 12:667–685

    Article  PubMed  CAS  Google Scholar 

  • Smith VC, Pokorny J (1975) Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm. Vision Res 15:161–171

    Article  PubMed  CAS  Google Scholar 

  • Spitzer H, Barkan Y (2005) Computational adaptation model and its predictions for color induction of first and second orders. Vision Res 45:3323–3342

    Article  PubMed  Google Scholar 

  • Stockman A, Brainard DH (2010) Color vision mechanisms. In: Bass M, Mahajan VN (eds) OSA handbook of optics. McGraw-Hill, New York, pp 11.11–11.104

    Google Scholar 

  • Stockman A, Sharpe LT (2000) The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype. Vision Res 40:1711–1737

    Article  PubMed  CAS  Google Scholar 

  • Westland S, Ripamonti C (2004) Characterization of cameras. In: Computational colour science: using MATLAB. Wiley, Chichester, pp 127–128

    Google Scholar 

  • Wyszecki G, Stiles WS (1982a) Theories and models of color vision. In: Color science: concepts and methods, quantitative data and formulae. Wiley, New York/Chichester, p 615

    Google Scholar 

  • Wyszecki G, Stiles WS (1982b) Colorimetry. In: Color science: concepts and methods, quantitative data and formulae. Wiley, New York/Chichester, pp 117–145

    Google Scholar 

  • Young T (1802) On the theory of light and colours. Philos Trans R Soc Lond 92:12–48

    Article  Google Scholar 

  • Zeki S (1993) A vision of the brain. Blackwell, Oxford/Boston

    Google Scholar 

  • Zhang J, Barhomi Y, Serre T (2012) A new biologically inspired color image descriptor. In: Fitzgibbon AW, Lazebnik S, Perona P, Sato Y, Schmid C (eds) ECCV 2012 – 12th European conference on computer vision, 7–13 Oct 2012. Springer, Florence, pp 312–324

    Google Scholar 

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Correspondence to C. Alejandro Parraga .

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Parraga, C.A. (2014). Color Vision, Computational Methods for. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_8-3

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_8-3

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

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Chapter history

  1. Latest

    Color Vision, Computational Methods for
    Published:
    15 September 2014

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_8-3

  2. Original

    Color Vision, Computational Methods for
    Published:
    24 February 2014

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_8-2