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Extension and Evaluation of Selection criteria for the Estimation of Spectral Data

机译:用于估计光谱数据的选择标准的扩展和评估

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Spectral data estimation from image data is an ill-posed problem since (i) due to the integral nature of imaging sensors, the same output can be obtained from an infinity of input signals and (ii) color signals are spectrally smooth in nature and, therefore, limit the number of linear independent data than can be collected. To enable the solution of these problems the solution's search space has to be constrained. The question that arises is how to select/parameterize these constraints? In this paper several model selection criteria are extended for spectral data estimation and evaluated in the context of spectral sensitivity function estimation of CCD sensors
机译:来自图像数据的光谱数据估计是由于成像传感器的积分性质,因此可以从输入信号的无限远地获得相同的输出,并且(ii)颜色信号在自然界中的光谱平滑是相同的输出,并且 因此,限制线性独立数据的数量比可以收集的数量。 为了启用这些问题的解决方案,必须限制解决方案的搜索空间。 出现的问题是如何选择/参数化这些约束? 在本文中,若干模型选择标准延长了光谱数据估计,并在CCD传感器的光谱灵敏度函数估计的上下文中评估

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