Colour space homography

对于像手机数码相机这样的商业“成像”产品，工程师们寻求简单而优雅的解决方案来解决难题。你会注意到新相机比旧相机拍出更好的照片。这部分是由于不断进步的硬件：有更快的处理器和更多的像素。然而，有一些重要的解决问题的见解，使人们有可能解决迄今为止棘手的问题。这方面的一个很好的例子是偏色问题（本提案的重点之一）。当你用相机拍照时，记录下来的颜色取决于场景中物体的颜色和（也许令人惊讶的是）光线的颜色。从物理上讲，当你在阳光直射下或在阴影中观看时，你的白色T恤是黄色和蓝色的（因为阳光和阴影分别是黄色和蓝色的）。我们看不到色偏，因为我们的视觉系统会分解光的颜色，同样，数码相机处理图像（大部分时间）会消除色偏。但是，如果你把今天的相机的输出与15年前的相机进行比较，现代相机在去除偏色方面要好得多。为什么？为什么？这是因为他们的聪明才智帮助工程师建立了正确推断主要光线颜色的系统。这项研究项目始于一项令人惊讶的新观察，我们相信这将有助于我们进一步改进成像产品，如彩色相机。我们第一次在图像中记录的颜色-通常是测量像素的红色，绿色和蓝色的三个R，G和B数字-与世界上点的3D位置以及这些点如何对应于图像中的像素位置之间建立了深层联系。考虑几何学，我们都知道火车轨道似乎在远处会聚，即使我们知道它们保持相同的距离（本质上，我们理解3D世界如何映射到2D图片）。在这项研究中，我们开始表明，颜色之间的关系-在不同的观看条件下看到的相同的彩色物体-是完全相同的关系，连接不同的观点-说的前面的建筑物-在几何。重要的是，现在我们已经将你在相机中看到的颜色与3D世界如何映射到图像联系起来，我们可以使用这个观察-我们可以利用几何领域的经验教训-来帮助我们解决一些彩色成像问题。首先，通过利用颜色空间单应性，相机制造商（和爱好者）将能够更准确地校准他们的相机，这将进一步优化图像颜色保真度。单应性也发挥了重要作用的色偏去除问题，在这个建议中，我们将开发一个单应性为基础的算法，将推进国家的art. Outside摄影，我们感兴趣的问题，如基于颜色的医疗诊断，例如，我们将致力于提高皮肤病变的自动识别。我们提出单应性也是解决高层次视觉任务的关键，包括识别，操纵和消除图像中的阴影。更新颖的是，我们发现，回答这个问题“我们如何找到一个彩色滤光片，当放置在相机前面时，使相机以类似于人类视觉系统的方式测量光线”也涉及到解决单应性。单应性与图像融合等问题有关-将100张图像融合成单一颜色摘要-在所谓的导数域中处理图像。在这里，图像被转换为边缘表示，然后在输出被重新整合之前对这些边缘进行操作。单应性有希望使重新整合过程更快，更不容易引入工件（现有技术的一个众所周知的问题）。

For commercial 'imaging' products like the digital camera in your phone, engineers seek simple and elegant solutions to hard problems. You will have noticed that newer cameras take better pictures than older ones. This is in part due to ever advancing hardware: there are faster processors and more pixels. Yet, there has been some important problem solving insights that make it possible to solve problems that were hitherto intractable. A good example of this is the problem of colour casts (one of the foci of this proposal). When you take a picture with a camera the colours that are recorded are dependent on the colour of objects in the scene and (perhaps surprisingly) on the colour of the light. Physically, your white T-shirt is yellowish and bluish when viewed in direct sunlight or when you are in the shadows (because sunlight and shadows are respectively yellowish and bluish). We do not see the colour casts as our vision system factors out the colour of the light and, likewise, digital cameras process images to (much of the time) remove colour casts. But, if you compare the outputs of cameras today with those 15 years ago, the modern era cameras are much better at removing colour casts. Why? because of clever insights that helped engineers build systems which correctly infer the colour of the prevailing light.This research project begins with a surprising new observation which we believe will help us improve still further imaging products such as colour cameras. We make, for the first time, a deep link between the colours recorded in image - typically three R,G and B numbers that measure the redness, greenness and blueness of a pixel - and the 3D locations of points in the world and how these points correspond to pixel locations in an image. Thinking about geometry, we are all aware that train tracks appear to converge in the distance even although we know they stay the same distance apart (intrinsically, we understand how the 3D world maps to 2D pictures). In this research we begin by showing that the relationship between the colours - same colourful object viewed under different viewing conditions - is exactly the same as the relationship that links different viewpoints - say of the front of a building - in geometry. Mathematically, this relationship is called an homography.Importantly, now that we have linked the colours you see in the camera to how the 3D world maps to images we can use this observation - we can exploit lessons learnt in the geometric domain - to help us solve some colour imaging problems. First, by exploiting colour space homography, camera manufacturers (and enthusiasts) will be able to more accurately calibrate their cameras and this will further optimize image colour fidelity. Homographies also play an important role the colour cast removal problem and in this proposal we will develop a homography-based algorithm that will advance the state of the art. Outside of photography, we are interested in problems such as colour based medical diagnosis e.g. we will be aiming to improve the automatic identification of skin lesions. We propose that homographies are also the key to solving high level vision tasks including identifying, manipulating and removing shadows from images.More novelly, we have found that answering the question "how do we find a coloured filter which, when place in front of a camera makes the camera measure light in a way that is similar to the human visual system' also involves solving for a homography. Homographies have a link to problems such as image fusion - fusing 100s of images into a single colour summary - which process images in the so-called derivative domain. Here images are transformed into an edge representation and then these edges are manipulated before an output is reintegrated. Homographies hold the promise of making the reintegration process faster and less prone to introducing artifacts (a well known problem of existing techniques).