In this thesis a novel method is proposed that makes use of multispectral and hyperspectral image data to generate a novel photometric-invariant spectral image. For RGB colour image, an illuminant-invariant image was constructed independent of the illuminant and shading. To generate this image either a set of calibration images was required, or entropy information from a single image was used. For spectral images we show that photometric-invariant image formation is in essence greatly simplified. We show that with the simple knowledge of peak sensor wavelengths we can generate a high-dimensional spectral invariant. The PSNR is shown to be high between the respective invariant spectral features for multispectral and hyperspectral images taken under different illumination conditions, showing lighting invariance for a per-pixel measure; and the s-CIELAB error measure shows that the colour error between the 3-D colour images used to visualize the output invariant high-D data is also small.
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Thesis advisor: Drew, Mark S.
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