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首页> 外文会议>Conference on Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts Ⅵ Jan 23-26, 2001, San Jose, USA >Color Printer Characterization Using Radial Basis Function Networks
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Color Printer Characterization Using Radial Basis Function Networks

机译：使用径向基函数网络的彩色打印机表征

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摘要

A key problem in multimedia systems is the faithful reproduction of color. One of the main reasons why this is a complicated issue are the different color reproduction technologies used by the various devices; displays use easily modeled additive color mixing, while printers use a subtractive process, the characterization of which is much more complex than that of self-luminous displays, In order to resolve these problems several processing steps are necessary, one of which is accurate device characterization. Our study examines different learning algorithms for one particular neural network technique which already has been found to be useful in related contexts, namely radial basis function network models, and proposes a modified learning algorithm which improves the colorimetric characterization process of printers. In particular our results show that is possible to obtain good performance by using a learning algorithm that is trained on only small sets of color samples, and use it to generate a larger look-up table (LUT) through use of multiple polynomial regression or an interpolation algorithm. We deem our findings to be a good start point for further studies on learning algorithms used in conjunction with this problem.
机译：多媒体系统中的关键问题是色彩的忠实再现。这是一个复杂问题的主要原因之一是各种设备使用了不同的色彩再现技术。显示器使用易于建模的加色混色，而打印机使用减法过程，其特征要比自发光显示器的特征复杂得多。为了解决这些问题，需要几个处理步骤，其中之一是准确的设备特征。我们的研究针对一种特定的神经网络技术检查了不同的学习算法，该算法已经在相关的上下文中有用，即径向基函数网络模型，并提出了一种改进的学习算法，可以改进打印机的比色表征过程。尤其是，我们的结果表明，通过使用仅对少量颜色样本进行训练的学习算法，并通过使用多项式回归或多项式回归来生成较大的查找表（LUT），就可以获得良好的性能。插值算法。我们认为我们的发现是进一步研究与该问题结合使用的学习算法的良好起点。

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