A supervised combination strategy for illumination chromaticity estimation
ACM Transactions on Applied Perception (TAP)
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Since more people choose the convenience of colour imaging over traditional greyscale imaging, colour is a very important and useful feature in the computer vision community. However, the changing colour of the object may lead to some problems if the illuminant colour changes, since any colour imaging device's response to light from imaged scenes depends on three factors: the nature of the illumination incident on the objects, the underlying physical property of the objects, and the sensor sensitivity of the imaging system itself. Therefore, as the urgent demands and challenges for emerging applications and higher quality for existing applications continue to grow, accurate reproduction of the object's colour becomes a more critical issue. This dissertation mainly addresses the problem of separating the illumination from the reflectance and extracting the accurate colour of the objects. We explore three colour constancy solutions whose final goal is to estimate the illumination colour from the image and recover the original objects' colour, assuming the scene is lit under one uniform illuminant. Particularly, a simple non-statistical estimation solution is proposed by identifying those grey surfaces upon a new colour coordinate system. For those scenes under multi-illuminations, we address the colour constancy problem by extending the standard Retinex with spatial edges that can be detected using a stereo vision technique. The basic idea of stereo vision is to infer the 3D structure and arrangement of a scene from two or more images captured at different viewpoints simultaneously, which is obviously impractical. Then we present a novel hybrid colour constancy solution for a single image under multi-illuminants. An efficient way of representing accurate colour is colour spectra. To reduce storage requirements and processing time, the finite dimensional model is applied to find the basis vectors and the corresponding coefficients. In addition to principal component analysis (PCA) and independent component analysis (ICA), two other nonnegative techniques, Nonnegative Matrix Factorization and Nonnegative ICA, are also tried. We also propose that the pseudo-inverse of the basis derived from these two nonnegative techniques can be used as physically realizable camera sensors.