Rank based spectral estimation

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The colours, or RGB pixels, recorded by a digital camera are the result of the interaction of the prevailing light in the scene striking and being reflected by objects and the characteristics of the camera itself. The complexity is such that different cameras see differently and no cameras see the world exactly as we do. You will have noticed this when looking at photos where sometimes the colours don't look right or the pictures captured by one camera look 'better' than another. Moreover, sometimes we see colours change dramatically. We have all probably observed that white clothes can look bluish under ultra violet light (say in a night club). But, in fact the colours we see change subtly, all the time, as we move from one light to another (which is why it is always a good idea to check the colour of your clothes outside the shop). Here, even small changes can lead to poor customer satisfaction or, potentially, in a medical imaging application the wrong diagnosis.Good pictures, by which we might mean accurate 'colour measurement' are possible if we know the spectral colour characteristics of a camera and/or the spectrum of light in a scene. While we can, in principle, measure these quantities the measurement is not easy to do so and is expensive (not easy as it requires considerable (Physics) lab time and expensive because spectral measurement devices cost many thousands of pounds). When measurement is not feasible, there do in fact exist methods for estimating (say) the spectrum of light in a scene. Yet, these methods only tend work if the camera is accurately calibrated first (a sort of chicken and the egg situation). Our 'Rank Based Spectral Estimation' Project aims to make it much easier to calibrate a camera or measure the illuminant in situ (and as such also make it easier to measure reflectance too)So, how does our method work. Well suppose we gave you 50 grey tiles all of which appeared to have a different brightness. It would be an easy task for you to rank them from darkest to brightest. But, now suppose we change the colour of the light. Depending on the spectral shape of the grey reflectances, the ranking order can change (sometimes considerably). No problem, it is a simple matter to reorder the tiles. Remarkably, for specially chosen reflectances, the rank order will strongly correlate with the spectral shape of the light. Thus a simple ranking experiment gives us a strong clue to the colour of the light. (And, if we knew the colour of the light we could, for example predict whether the colour of our clothes might change when we go outdoors.)The Rank Based Spectral Estimation project aims to take this simple ranking idea and provide simple, and accurate, estimation tools for deriving the spectral shape of the prevailing light, the spectral characteristics of a camera and the spectral reflectances of surfaces. At the heart of our method is a specially designed reflectance target containing many reflectances (whose design is part of the proposed research). Ranking these reflectances will allow us to accurately estimate the light spectrum and the spectral attributes of a camera. Accurate spectral estimates are required in many applications from photography, through, visual inspection to forensic imaging and telepresence (e.g. remote diagnosis).Remarkably, we believe the methods we develop will also prove useful in understanding how we see. Indeed, it is very likely that you see the world a little differently than I do. Yet estimating an individual's spectral response is notoriously difficult. To the extent it can be done at all, it requires many hours of (tedious) detailed visual experiments. Through ranking it will be possible to uncover an observers spectral response (technically called 'colour matching curves') quickly and simply. We simply ask the observer to carry out a simple ranking of the kind mentioned above.