Centro de Investigaciones en Optica, Loma del Bosque 115, Lomas del Campestre, Leon, Gto., Mexico 37150.
J Biomed Opt. 2013 Apr;18(4):046005. doi: 10.1117/1.JBO.18.4.046005.
Considering the high degree of correlation in the visible spectrum, the principal wavelengths from spectral measurements of radiance recorded in spectral images were selected using a method based on principal components analysis (PCA). It seems to be that this is the first time that, instead of using spectra, data is taken directly from the "slices" of spectral images; the method has the advantage of preserving the structure of the original data in the reduced data set. A "true" dimensionality of five wavelengths resulted for all the analyzed images. The averages of the selected wavelengths for 10 spectral images produced good results for a human observer. These results were possible using only four wavelengths. Though PCA by itself is not able to include the impact of specific sensors on the selection of basis functions, results suggest that the variable selection method used in this work (which is not just PCA) yielded objective information of the structure of the physical stimuli (i.e., the spectral structures) that have been shaping the visual systems of animals and insects since many years ago.
考虑到可见光谱中的高度相关性,使用基于主成分分析(PCA)的方法从记录在光谱图像中的辐射光谱测量中选择主波长。这似乎是第一次直接从光谱图像的“切片”中获取数据,而不是使用光谱;该方法的优点是在减少的数据集中保留原始数据的结构。对于所有分析的图像,都得到了“真实”的五个波长维度。对于 10 个光谱图像的选定波长平均值,人类观察者的结果很好。仅使用四个波长就可以实现这些结果。尽管 PCA 本身无法包括特定传感器对基函数选择的影响,但结果表明,本工作中使用的变量选择方法(不仅仅是 PCA)提供了对物理刺激结构的客观信息(即,光谱结构),这些结构多年来一直在塑造动物和昆虫的视觉系统。