Rank based spectral estimation

数码相机记录的颜色或RGB像素是场景中的主要光线与相机本身的特性相互作用的结果。复杂性是这样的，不同的相机看到不同的，没有相机看到的世界完全一样，我们做的。你会注意到这一点时，有时看照片的颜色不看权利或由一个相机拍摄的照片看起来比另一个“更好”。此外，有时我们会看到颜色发生戏剧性的变化。我们都可能观察到，白色衣服在紫外线下会看起来有点蓝（比如在夜总会）。但是，事实上，当我们从一个灯移动到另一个灯时，我们看到的颜色总是在微妙地变化（这就是为什么在商店外检查衣服的颜色总是一个好主意）。在这里，即使是很小的变化也可能导致客户满意度下降，或者在医学成像应用中可能导致错误的诊断。如果我们知道相机的光谱颜色特性和/或场景中的光谱，那么就可以获得准确的“颜色测量”。虽然原则上我们可以测量这些量，但测量并不容易，而且很昂贵（不容易，因为它需要相当多的（物理学）实验室时间，而且昂贵，因为光谱测量设备花费数千英镑）。当测量不可行时，实际上确实存在用于估计场景中的光谱的方法。然而，这些方法只有在相机首先被精确校准的情况下才能起作用（一种鸡和蛋的情况）。我们的“基于秩的光谱估计”项目旨在使校准相机或原位测量光源变得更加容易（因此也使测量反射率变得更加容易）。假设我们给你50个灰色的瓷砖，它们看起来都有不同的亮度。对你来说，从最黑暗到最明亮排列它们是一件容易的事情。但是，现在假设我们改变光的颜色。根据灰色反射率的光谱形状，排名顺序可以改变（有时相当大）。没问题，这是一个简单的事情重新排序瓷砖。值得注意的是，对于特别选择的反射率，等级顺序将与光的光谱形状密切相关。因此，一个简单的排名实验给了我们一个强有力的线索，光的颜色。(And例如，如果我们知道光的颜色，我们就可以预测当我们走到户外时，我们衣服的颜色是否会改变。Rank Based Spectral Estimation项目旨在采用这种简单的排名思想，并提供简单，准确的估计工具，用于导出主要光线的光谱形状，相机的光谱特性和表面的光谱反射率。在我们的方法的核心是一个专门设计的反射目标包含许多反射（其设计是拟议的研究的一部分）。对这些反射率进行排序将使我们能够准确地估计光谱和相机的光谱属性。从摄影到视觉检查，再到法医成像和远程呈现（例如远程诊断），许多应用都需要精确的光谱估计。值得注意的是，我们相信我们开发的方法也将有助于理解我们是如何看到的。事实上，很可能你看世界的方式和我有点不同。然而，估计一个人的光谱反应是出了名的困难。在某种程度上，它可以做到，它需要许多小时的（乏味的）详细的视觉实验。通过排序，可以快速简单地揭示观察者的光谱响应（技术上称为“颜色匹配曲线”）。我们只是要求观察者进行一个简单的排序，就像上面提到的那样。

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.