Study on spectral reconstruction algorithm based on kernel entropy component analysis
2018
The principal component analysis method (PCA) and the kernel entropy component analysis method (KECA) are used to construct the spectral reflectance, and study the color reproduction. . This study compares reconstruction precision through the spectral reflectance reconstruction methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and kernel entropy component analysis (KECA). Experimental results show that spectral reconstruction algorithm based on KECA is superior than PCA and KPCA in chromaticity precision and spectral precision. It has certain application value for the true color reproduction of the object surface.
Keywords:
- Kernel (linear algebra)
- Principal component analysis
- Chromaticity
- Reconstruction algorithm
- Component analysis
- Reflectivity
- Kernel principal component analysis
- Artificial intelligence
- Pattern recognition
- Mathematics
- reconstruction method
- Algorithm
- kernel entropy component analysis
- color reproduction
- spectral reconstruction
- Correction
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- Cite
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