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A method for selecting training samples based on camera response

机译:一种基于相机响应选择训练样本的方法

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摘要

In the process of spectral reflectance reconstruction, sample selection plays an important role in the accuracy of the constructed model and in reconstruction effects. In this paper, a method for training sample selection based on camera response is proposed. It has been proved that the camera response value has a close correlation with the spectral reflectance. Consequently, in this paper we adopt the technique of drawing a sphere in camera response value space to select the training samples which have a higher correlation with the test samples. In addition, the Wiener estimation method is used to reconstruct the spectral reflectance. Finally, we find that the method of sample selection based on camera response value has the smallest color difference and root mean square error after reconstruction compared to the method using the full set of Munsell color charts, the Mohammadi training sample selection method, and the stratified sampling method. Moreover, the goodness of fit coefficient of this method is also the highest among the four sample selection methods. Taking all the factors mentioned above into consideration, the method of training sample selection based on camera response value enhances the reconstruction accuracy from both the colorimetric and spectral perspectives.
机译:在光谱反射率重建的过程中,样品的选择对构建模型的准确性和重建效果起着重要作用。提出了一种基于相机响应的样本选择训练方法。已经证明,照相机响应值与光谱反射率具有紧密的相关性。因此,在本文中,我们采用在摄像机响应值空间中绘制球体的技术来选择与测试样本具有更高相关性的训练样本。另外,维纳估计方法用于重建光谱反射率。最后,我们发现,与使用全套Munsell色图,Mohammadi训练样本选择方法和分层方法相比,基于相机响应值的样本选择方法在重建后具有最小的色差和均方根误差。抽样方法。此外,在四种样本选择方法中,该方法的拟合系数的优度也最高。考虑到上述所有因素,从相机响应值训练样本选择的方法从比色和光谱角度都提高了重建精度。

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