19 May 1999 Bootstrapping color constancy
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Bootstrapping provides a novel approach to training a neural network to estimate the chromaticity of the illuminant in a scene given image data alone. For initial training, the network requires feedback about the accuracy of the network's current results. In the case of a network for color constancy, this feedback is the chromaticity of the incident scene illumination. In the past, prefect feedback has been used, but in the bootstrapping method feedback with a considerable degree of random error can be used to train the network instead. In particular, the grayworld algorithm, which only provides modest color constancy performance, is used to train a neural network which in the end performs better than the grayworld algorithm used to train it.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian V. Funt, Brian V. Funt, Vlad C. Cardei, Vlad C. Cardei, } "Bootstrapping color constancy", Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); doi: 10.1117/12.348463; https://doi.org/10.1117/12.348463

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