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  4. Neural Network for Color Constancy

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Neural Network for Color Constancy
Volume 11, Issue 2 (2000), pp. 219–232
Rytis Stanikūnas   Henrikas Vaitkevičius  

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https://doi.org/10.3233/INF-2000-11208
Pub. online: 1 January 2000      Type: Research Article     

Received
1 February 2000
Published
1 January 2000

Abstract

Color constancy is the perceived stability of the color of objects under different illuminants. Four-layer neural network for color constancy has been developed. It has separate input channels for the test chip and for the background. Input of network was RGB receptors. Second layer consisted of color opponent cells and output have three neurons signaling x, y, Y coordinates (1931 CIE). Network was trained with the back-propagation algorithm. For training and testing we used nine illuminants with wide spectrum. Neural network was able to achieve color constancy. Input of background coordinates and nonlinearity of network have crucial influence for training.

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Keywords
color vision color constancy neural networks computational vision

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INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
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