Abstract: Comparison in the RGB domain is not suitable for precise color matching, due to the strong dependency of this domain on factors like spectral power distribution of the light source and object geometry. We have studied the use of multispectral or hyperspectral images for color matching, since it can be proven that hyperspectral images can be made independent of the light source and object geometry. Hyperspectral images have the disadvantages that they are large compared to regular RGB-imags, which makes it infeasible to use them for image matching across the Internet. For red roses, it is possible to reduce the large number of bands of the spectral images to only three bands, the same numbers of an RGB-image, using Principal Component Analysis, while maintaining 99 percent of the original variation. The obtained PCA-images of the roses can be matched using for example histogram cross correlation. From the principal coordinates plot, obtained from the histogram similarity matrices of twenty images of red roses, the discriminating power seems to be better for normalized spectral images than for color constant spectral images and RGB-images, the latter being recorded under highly optimized standard conditions. !9
展开▼
