Graham D. Finlayson

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ÐThis paper considers the problem of illuminant estimation: how, given an image of a scene, recorded under an unknown light, we can recover an estimate of that light. Obtaining such an estimate is a central part of solving the color constancy problemÐthat is of recovering an illuminant independent representation of the reflectances in a scene. Thus, the(More)
This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then(More)
We develop sensor transformations, collectively called spectral sharpening, that convert a given set of sensor sensitivity functions into a new set that will improve the performance of any color-constancy algorithm that is based on an independent adjustment of the sensor response channels. Independent adjustment of multiplicative coefficients corresponds to(More)
In computational terms we can solve the color constancy problem if device red, green, and blue sensor responses, or RGB's, for surfaces seen under an unknown illuminant can be mapped to corresponding RGB's under a known reference light. In recent years almost all authors have argued that this three-dimensional problem is too hard. It is argued that because(More)
Image colors are biased by the color of the prevailing illumination. As such the color at pixel cannot always be used directly in solving vision tasks from recognition, to tracking to general scene understanding. Illuminant estimation algorithms attempt to infer the color of the light incident in a scene and then a color cast removal step discounts the(More)