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

数字成像、人类感知、计算机视觉和AR/VR/MR的计算颜色方法这项建议是关于不同的“观察者”--不同的人、不同的相机--以及在不同的光线(日光、LED、荧光灯、钨)下如何感知物体的颜色。色彩是一个非常跨学科的领域，涉及心理学、哲学、化学、物理学和计算机科学。我理解颜色的方法是将颜色感知视为一个计算过程。因此，在我的实验室开发的颜色感知模型被制定为算法，可以进行测试，一方面是它们是否只是提供预期的结果，其次是它们的运行方式是否与心理学家进行的心理物理实验中关于人类颜色感知的已知方式一致。理解和模拟颜色感知的基本困难在于，因为人类只有三种颜色敏感锥体，所以进入眼睛的光的波长和感知的颜色之间没有一一对应的关系。还有许多其他的困难，比如相同的反射光谱在不同的环境中可能看起来不同，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、纺织业、照明行业、颜色的科学使用(例如，在医疗应用中)、艺术品的数字保存以及最近增强/混合现实应用中的颜色精度带来了问题。本提案中的研究项目涉及直接应用于所有这些技术领域的色彩科学的基本问题。拟议研究的目标建立在我的实验室最近的进展基础上，这些进展包括同色异谱、颜色恒定及其限制、建立所有理论上可能的颜色集、评估灯光的显色特性以及评估数码相机的颜色精度。拟议的目标包括：(I)一种新的照度不变的目标分类和识别方法，对机器学习具有普遍意义；(Ii)建立一个包含复杂、空间变化的照明的场景的多光谱图像的准确地面真实的数据集，供颜色研究界使用；(Iii)为增强现实应用提供颜色测量和感知上的准确颜色；(Iv)进一步发展关于颜色辨别如何随色调变化的新理论；以及(V)从压缩感知的角度理解人类视觉。研究预算主要用于培训高素质人员；即支持学生工资和他们的会议旅费。我以前的学生现在都在彩色成像领域取得了成功的职业生涯。

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.