Computational Colour Approach to Digital Imaging, Human Perception, Computer Vision and AR/VR/MR

这个提案是关于不同的“观察者”——不同的人，不同的相机——在不同的灯光下（日光，LED，荧光灯，钨）如何感知物体的颜色。色彩是一个涉及心理学、哲学、化学、物理和计算机科学的跨学科领域。我理解颜色的方法是将颜色感知视为一个计算过程。因此，在我的实验室中开发的颜色感知模型被制定为算法，可以根据它们是否简单地提供预期结果进行测试，其次，根据它们是否以一种与心理学家进行的心理物理实验中已知的人类颜色感知一致的方式进行测试。理解和模拟颜色感知的根本困难在于，因为人类只有3种对颜色敏感的视锥细胞，所以进入眼睛的光的波长和感知到的颜色之间没有一对一的对应关系。还有许多其他的困难，例如，相同的反射光光谱在不同的环境下看起来可能不同，这些都有助于颜色成为一个迷人的研究领域。然而，这些困难给数码相机行业、数字印刷行业、数字显示行业、纺织行业、照明行业、色彩的科学使用（例如，在医疗应用中）、艺术品的数字保存以及最近增强/混合现实应用中的色彩准确性带来了问题。本提案中的研究项目解决了色彩科学的基本问题，这些问题直接应用于所有这些技术领域。拟议的研究目标建立在我实验室最近的进展基础上，包括对同色性、色彩恒常性及其限制的研究，建立所有理论上可能的颜色的集合，评估光的显色性，以及评估数码相机的色彩准确性。提出的目标包括：(i)一种新的光源不变的对象分类和识别方法，对机器学习具有普遍意义；（ii）建立一个包含复杂、空间变化的照明场景的多光谱图像的准确地面真值数据集，供色彩研究界使用；（iii）为增强现实应用提供色度和感知上准确的颜色；（iv）进一步发展一种关于颜色辨别如何随色调变化的新理论；(v)从压缩感知的角度理解人类视觉。科研经费主要用于培养高素质人才；即支持学生工资和会议旅费。我以前的学生现在都在彩色成像领域有了成功的事业。

Computational Colour Approach to Digital Imaging, Human Perception, Computer Vision and AR/VR/MR This proposal is about how the colours of objects are perceived by different `observers'-different people, different cameras-and under different lights (daylight, LED, fluorescent, tungsten). Colour is a very interdisciplinary field touching on psychology, philosophy, chemistry, physics, and computer science. My approach to understanding colour is to view colour perception as a computational process. As such, the models of colour perception developed in my laboratory are formulated as algorithms that can be tested both in terms of whether or not they simply provide the expected results, and secondly in terms of whether they operate in a way that is congruent with what is known about human colour perception from the psychophysical experiments conducted by psychologists. The fundamental difficulty in understanding and modeling colour perception is that because humans have only 3 types of colour-sensitive cones there is no one-to-one correspondence between the wavelengths of light entering the eye and perceived colour. There are many other difficulties too, such as how the same reflected-light spectrum may look different in different contexts, and these all contribute to colour being a fascinating field of research. These difficulties, however, present problems for the digital camera industry, the digital printing industry, the digital display industry, the textile industry, the lighting industry, the scientific use of colour (e.g., in medical applications), the digital preservation of artwork, and most recently to colour accuracy in augmented/mixed reality applications. The research projects in this proposal address fundamental issues of colour science that have direct application to all these technology areas. The objectives of the proposed research build on the recent progress in my laboratory that include work on metamerism, colour constancy and the limits thereof, establishing the set of all theoretically-possible colours, evaluating the colour rendering properties of lights, and evaluating the colour accuracy of digital cameras. The proposed objectives include: (i) a new illuminant-invariant approach to object classification and recognition with implications for machine learning generally; (ii) building a dataset with accurate ground truth of multispectral images of scenes containing complex, spatially-varying lighting for use by the colour research community; (iii) providing colorimetrically and perceptually accurate colour for augmented reality applications; (iv) further developing a new theory of how colour discrimination varies with hue; and (v) understanding human vision in terms of compressive sensing. The research budget is chiefly for the training of highly qualified personnel; namely, support of student salaries and their conference travel. My previous students now all have successful careers in the colour-imaging field.