Prediction of RGB camera values by means of artificial neural networks
ČlánekStatus neznámýpeer-reviewedpublishedDatum publikování
2013
Vedoucí práce
Oponent
Název časopisu
Název svazku
Vydavatel
University of Pardubice
Abstrakt
Artificial neural networks (ANN) enable modelling of complex nonlinear systems
that cannot be easily described using formal equations and have been
implemented in many fields of science and technology for pattern recognition,
clustering or data fitting. The goal of our study was to create a system that
transforms XYZ and L*a*b* values into arbitrary camera RGB values in stable
— but without strict knowledge of — photographing conditions, by means of the
ANN data fitting ability. We adopted a two layer feed-forward neural network with
sigmoid hidden and linear output neurons, that can fit multi-dimensional mapping
problems quite well, when using enough neurons in the hidden layer and being fed
by congruent learning set of data. The network was trained with Levenberg–
Marquardt backpropagation algorithm. Learning data sets consisted of input
XYZ or L*a*b* values and output RGB values. Input data were calculated from
the reflectance values of Gretag Macbeth Digital ColorChecker SGtest chart
obtained by spectrophotometric measurements, by taking into account three different standard illuminants (A, D50 and D65) and two standard colorimetric
observers (2 ° and 10 °). Output data were RGB values of test chart ColorChecker
SG acquired by Nikon D50 digital camera. Our goal was to find answers to
several questions, such as what is an optimal number of hidden layer neurons,
what degree of accuracy can we obtain by training ANN with a limited number of
color samples, how does number of neurons affect ANN learning time and also
which type of input data is more suitable for the prediction of RGB values. Since
each ANN learning epoch starts with a random weight distribution and random
training, validation and testing data selection, every learning cycle stopped in its
local minimum. To assess the representative values of difference between the
actual and the predicted values, learning cycle for each number of hidden layer
neurons and for each learning data set was repeated many times and average
ANN training time and average, median and minimal error rates for training,
validation and testing data were recorded.
Rozsah stran
p. 185–194
ISSN
1211-5541
Trvalý odkaz na tento záznam
Projekt
Zdrojový dokument
Scientific papers of the University of Pardubice. Series A, Faculty of Chemical Technology. 19/2013
Vydavatelská verze
Přístup k e-verzi
open access
Název akce
ISBN
978-80-7395-748-3