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长春光学精密机械与物... [1]
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会议论文 [1]
发表日期
2006 [1]
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Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE)
会议论文
ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China
Zhao H.
;
Li Z.
;
Wang C.
;
Sun J.
;
Zhou F.
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提交时间:2013/03/25
Munsell color system is selected to establish the mutual conversion between RGB and L*a*b* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card
XYZ value is gotten from the color luminance meter
the training error is 0.000748566
it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value
the color picture captured from CCD camera is expressed for RGB value as the input of neural network
and the L*a*b* value converted from XYZ value is regarded as the real color value of target card
which the difference is not obvious comparing with forecast result
the maximum is 5.6357 NBS
namely the output of neural network. The neural network of two hidden-layers is considered
the minimum is 0.5311 NBS
so the second general revolving combination design is introduced into optimizing the structure of neural network
and the average of color difference is 3.1744 NBS.
which can carry optimization through unifying project design
data processing and the precision of regression equation. Their mathematics model of encoding space is gained
and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm
optimization solution is gotten
and function value of the goal is 0.0007168. The neural network of the optimization solution is trained
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