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Information limits on neural identification of colored surfaces in natural scenes

Published online by Cambridge University Press:  05 April 2005

DAVID H. FOSTER ,
SÉRGIO M.C. NASCIMENTO  and
KINJIRO AMANO
DAVID H. FOSTER
Affiliation:
Visual and Computational Neuroscience Group, University of Manchester Institute of Science and Technology, Manchester, UK
SÉRGIO M.C. NASCIMENTO
Affiliation:
Department of Physics, Gualtar Campus, University of Minho, Braga, Portugal
KINJIRO AMANO
Affiliation:
Visual and Computational Neuroscience Group, University of Manchester Institute of Science and Technology, Manchester, UK
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Abstract

If surfaces in a scene are to be distinguished by their color, their neural representation at some level should ideally vary little with the color of the illumination. Four possible neural codes were considered: von-Kries-scaled cone responses from single points in a scene, spatial ratios of cone responses produced by light reflected from pairs of points, and these quantities obtained with sharpened (opponent-cone) responses. The effectiveness of these codes in identifying surfaces was quantified by information-theoretic measures. Data were drawn from a sample of 25 rural and urban scenes imaged with a hyperspectral camera, which provided estimates of surface reflectance at 10-nm intervals at each of 1344 × 1024 pixels for each scene. In computer simulations, scenes were illuminated separately by daylights of correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were sampled randomly in each scene and identified according to each of the codes. It was found that the maximum information preserved under illuminant changes varied with the code, but for a particular code it was remarkably stable across the different scenes. The standard deviation over the 25 scenes was, on average, approximately 1 bit, suggesting that the neural coding of surface color can be optimized independent of location for any particular range of illuminants.

Keywords

Information capacityNatural scenesColor constancyColor visionSpectral sharpeningCone opponency
Type
Research Article
Information
Visual Neuroscience , Volume 21 , Issue 3 , May 2004 , pp. 331 - 336
Copyright
© 2004 Cambridge University Press

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