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- W71293344 abstract "Spectral imaging has been drawing attentions in skin segmentation related medical and computer vision applications. Usually spectral images contain radiance spectra or approximated reflectance spectra of objects. The reflectance spectrum is independent of illuminations, whereas the radiance spectrum is dependent on the illumination that was used during the measurement. Recently, the general illumination is using increasingly LED lights that have different spectral characteristics when compared to incandescent or fluorescent lights. In this paper, we studied effects of illuminations in unsupervised pixel-based skin segmentation with the Spectral Clustering (SC) algorithm based on two sets of skin spectral images, human hand and face. We adopted the eigengap heuristic to select the illumination in skin segmentation with the SC algorithm. Spectral radiance images under three CIE standard illuminants (A, D65, and F11), and two white LED illuminations, were respectively simulated from corresponding spectral reflectance images. The experimental results showed that CIE D65 illuminant return the highest average segmentation accuracies with 2.1% point and 3.4% point higher than the lowest results of illuminant CIE A in hand images and illuminant CIE F11 in face images, respectively. The accuracies of two LED illuminations are also high with around 1.5% point and 1.0% point decreases in average from the accuracy of illuminant CIE D65." @default.
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- W71293344 date "2013-01-01" @default.
- W71293344 modified "2023-09-27" @default.
- W71293344 title "Segmentation of Skin Spectral Images Using Simulated Illuminations" @default.
- W71293344 cites W2016857533 @default.
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- W71293344 doi "https://doi.org/10.1007/978-3-642-40246-3_34" @default.
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