Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.
@article{Shi:11,
author = {Lilong Shi and Weihua Xiong and Brian Funt},
journal = {J. Opt. Soc. Am. A},
keywords = {Digital image processing; Vision, color, and visual optics ; Color; Color, measurement ; Color vision; Camera calibration; Illumination; Image registration; Interpolation; Light sources; Neural networks},
number = {5},
pages = {940--948},
publisher = {Optica Publishing Group},
title = {Illumination estimation via thin-plate spline interpolation},
volume = {28},
month = {May},
year = {2011},
url = {https://opg.optica.org/josaa/abstract.cfm?URI=josaa-28-5-940},
doi = {10.1364/JOSAA.28.000940},
abstract = {Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.},
}