IDENTIFICATION OF COLOR UNIFORMITY DEFECT ON THE ELECTRONIC DISPLAYS BY LEARNING THE HUMAN PERCEPTION RECORDS
Mauridhi Hery Purnomo, Toshio Asano, Eiji Shimizu · IEEJ Transactions on Electronics Information and Systems · 1998
This article explores a proposed method for identifying and classifying the color uniformity defect on the electronic displays. A neural network learning approach utilizing the backpropagation learning algorithm is employed to search the dissimilarity of the color display condition. The color uniformity defect image perception among several conditions of some observations by human eyesight is used to supervise the training data of the neural networks. A supervisor of the training data is obtained by human expert eyes evaluate the white uniformity grade and compare with the standard grade, then labeling the perception grade into some certainty values. The trained network is used to identify and classify the grades level of the color uniformity defect on the electronic displays. For the experimental purpose, a simulation program is developed to generate the color uniformity defect on the monitor display. We make a comparison with a classical regression analysis method for validation of the proposed method.