In this paper we begin by observing that the function defining the Vora-Value is equivalent to the Luther-condition optimization if we use the orthonormal basis of the XYZ color matching functions, i. e. we linearly transform the XYZ sensitivities to a set of orthonormal basis.
This paper reviews the second challenge on spectral reconstruction from RGB images, i. e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.
Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems.
We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.
Recent work by Finlayson, Interface Focus, 2018 showed that a bias correction function can be formulated as a projective transform because the magnitude of the R, G, B illumination vector does not matter to the AWB procedure.