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Adaptive Statistical Methods for Optimal Color Selection and Spectral Characterization of Color Scanners and Cameras

机译：用于彩色扫描仪和照相机的最佳颜色选择和光谱表征的自适应统计方法

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摘要

The aim of the study was to create an improved colori-metric and broadband spectral characterization for scanners and cameras. In such characterization, selecting an adequate number of color samples of known reflection spectra is necessary. And though countless sample data sets are available, the properties required of a data set for such optimal characterization remain elusive. Therefore a new methodology was required to address the characterization task. Such a characterization method is introduced in this article and is based on statistical classification of the colorimetric and broadband spectral properties of color sample sets. It introduces and effectively utilizes both the reflectance spectrum of the color sample and the spectral power distribution of the source. However it is shown that characterization methods based on a regression model can be used only if the conditions of the regression model are satisfied and that most statistical estimation errors are caused by conditions of the regression model not being satisfied (for instance heteroscedasticity, autocorrelation, multicollinearity). Nevertheless, the method introduced selects optimal representative color samples, so that with these samples the spectral responsivity of the detector can be estimated more precisely. The selection method is self-adaptive. If the reflectance spectra of the color samples and the spectral power distribution of the source are known, the optimal number of color samples, the number of principal eigenvectors, etc., are automatically set up according to the given a priori information, and the responsivity curves are determined where, the given z target function [see Eq. (5)] is minimal. The study has shown that the estimation error of broadband characterization can be decreased significantly if an optimal set of color samples is selected using these statistical methods. If there is more a priori information (for instance the spectral power distribution of the source of the scanner) the estimation error can be further decreased.
机译：该研究的目的是为扫描仪和照相机创建一种改进的比色和宽带光谱表征。在这种表征中，必须选择足够数量的已知反射光谱的颜色样本。尽管有无数的样本数据集可用，但实现最佳表征所需的数据集属性仍然难以捉摸。因此，需要一种新的方法来解决表征任务。本文介绍了这种表征方法，该方法基于颜色样本集的比色和宽带光谱特性的统计分类。它引入并有效利用了颜色样本的反射光谱和光源的光谱功率分布。然而，事实表明，只有在满足回归模型的条件且大多数统计估计误差是由于不满足回归模型的条件（例如异方差，自相关，多重共线性）时，才可以使用基于回归模型的表征方法）。然而，引入的方法选择了最佳的代表性颜色样本，因此使用这些样本可以更精确地估计检测器的光谱响应度。选择方法是自适应的。如果已知颜色样本的反射光谱和光源的光谱功率分布，则会根据给定的先验信息和响应度自动设置最佳颜色样本数量，主特征向量数量等。在给定的z目标函数的位置确定曲线。 （5）]是最小的。研究表明，如果使用这些统计方法选择最佳的颜色样本集，则宽带表征的估计误差可以大大降低。如果存在更多先验信息（例如，扫描仪光源的光谱功率分布），则可以进一步降低估计误差。

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  • 来源
    《Journal of Imaging Science and Technology》 |2009年第1期|010501.1-010501.10|共10页
  • 作者

    Zsolt Tibor Kosztyan; Janos Schanda;

  • 作者单位

    Department of Management, University of Pannonia, Veszprem, Hungary;

    Department of Image Processing and Neurocomputing, University of Pannonia, Veszprem, Hungary;

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