"Physics-based colour models in computer vision, colour science, graphics and multimedia"

(A.)计算摄影：(I)我最近生成了重新整合的图像，从图像所需的梯度场对开始，这是一个长期存在的问题，伪影大大减少。(Ii)在2011年的一项专利中，我展示了如何将对比度从高清图像数据向下传输到低维图像，例如，将对比度从RGB红外图像向下传输到彩色图像。这项工作使用了(I)中的专利；然而，找到的解决方案在图像上是全局的。局部映射原则上应该更有效，我提出了一个变分方程解。(Iii)然后可以解决其他问题，例如没有光晕伪影的图像锐化和各向异性平滑(“去皱纹”)。B.)图形/可视化：(I)在处理多照明图像集时，需要照明方向。但对于“在野外”的物体，方向是不准确的，所以我希望发现它们，以及寻找灯光的可见性子空间。(Ii)上述资料可用于发现博物馆借出的艺术品的损坏情况。(Iii)在高清医学图像可视化方面，我已经生成了具有感知意义的颜色输出。然而，剪裁经常发生。如果将色域拟合包括在优化中，这个问题可以在生成可视化的同时得到解决。(C)计算机视觉：(I)无阴影的固有(光照不变)图像利用特征空间中的熵作为关键步骤；我建议使用视频信息，随着时间的推移积累证据。(2)另一个方向是将固有图像特征应用于皮肤镜图像：固有图像可用于消除光色和强度变化。我还建议删除肤色，对于与肤色无关的彩色图像病变图集。D.)多媒体：如果对大型剧院的演员进行2-D的人群跟踪，就可以产生音频深度和平移效果。E.)颜色：(I)相当大比例的纸张不正确地使用颜色。我提出了一个出版物和参考代码集，用于相机校准的层次结构，从完整到简单，指导研究人员如何进行。(Ii)色彩平衡对于相机数据至关重要。最近，我向苹果的iPhone摄像头团队提交了一种新的算法，并提议对此进行进一步研究。

A.) Computational Photography: (i) I have recently generated re-integrated images starting with an image's desired gradient field pair, a longstanding problem, with greatly reduce artifacts. (ii) In a 2011 patent I show how to transfer the contrast from hi-D image data down to lower dimensional images, e.g. transfer the contrast from RGB+infrared down to colour images. This work uses the patent in (i); however, the solution found is global over the image. A local mapping should in principle be much more effective, and I propose a variational-equation solution. (iii) Then other problems could be attacked, such as image sharpening without halo artifacts, and anisotropic smoothing ("wrinkle remover"). B.) Graphics/Visualization: (i) In work on multiple-lighting image sets, lighting directions are required. But for "in the wild" objects directions are inaccurate, so instead I wish to discover them, along with finding lights' visibility subspaces. (ii) The above could be used to discover damage to artworks lent out by museums. (iii) In hi-D medical image visualization, I have generated perceptually meaningful colour outputs. However, clipping often occurs. If gamut-fitting is included into the optimization this problem could be solved whilst generating the visualization. C.) Computer Vision: (i) Shadow-free intrinsic (illumination invariant) images utilize the entropy in a feature space as a crucial step; I propose using video information, accumulating evidence over time. (ii) Another direction would apply the intrinsic image feature to dermoscopy images: the intrinsic image can be used to eliminate light-colour and intensity changes. I also propose removing skin colour, for a colour-image lesion atlas independent of skin colour. D.) Multimedia: If actors in large theatres were crowd-tracked in 2-D, audio depth plus a panning could be generated. E.) Colour: (i) A substantial proportion of papers use colour incorrectly. I propose a publication and reference codeset for a hierarchy of camera calibrations, from complete to simple, for guiding researchers in how to proceed. (ii) Colour Balance is critical for camera data. I recently presented a new algorithm to the iPhone Camera team at Apple and propose further investigating this.