阴极射线管色度转换的神经网络模型  

Cathode ray tube color conversion model by use of neural networks

作　　者：楼文高[1] 王晓红[1] 匡罗平[2] 

机构地区：[1]上海理工大学出版印刷学院,上海200093 [2]上海理工大学管理学院,上海200093

出　　处：《上海理工大学学报》2006年第1期35-38,共4页Journal of University of Shanghai For Science and Technology

基　　金：上海市重点学科资助项目;上海市教委高等学校科学技术发展基金资助项目(01H03);上海高校优青后备人选培养计划资助项目

摘　　要：利用神经网络技术实现了从阴极射线管（CRT）的R、G和B空间到CIE的标准色度空间的转换。用拟牛顿法训练网络模型，建立了从CRT的R、G和B到CIE的X、Y和Z色度空间变换的3-10-10-3神经网络模型．采用7点LOG空间分布方案的343个训练样本建模的试验表明，收敛性与训练时间及模型精度均优于前人采用3～4个隐层的方案，343个训练样本、216个检验样本和64组测试样本的平均转换精度分别为0．6个CIELUV色差单位，说明该模型的泛化能力很好。The color calibration in CRT （cathode ray tube） space is the base of color standard in computers. The color notation conversion from the RGB space of the CRT to the XYZ space of CIE system is carried out by using neural network. The neural network topology with a few neurons in two hidden layers,and quasi-Newton method are applied. With the neural network based model,343 training set data and 216 verification set data according to the principle with LOG space and 64 data sets according to even distribution are used to verify the training results and test the modeling performance. The case study shows that the converging speed, the training time and the accuracy of the model with two hidden layers are very satisfactory. The average precision of the color notation conversion of the model established is less than 0.6 CIELUV unit.

关 键 词：CRT色度 计算机颜色 神经网络 颜色空间变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]