Image constraints on the perception of surfaces and materials

人类可以从视觉上感知3D形状、颜色、光泽和不透明度不同的物体，但如何从所看到的图像中恢复这些信息仍然是一个谜。大脑必须通过具有不同表面属性的光的相互作用来利用对图像形成方式的限制。这个项目旨在确定大脑用来推断这种物质多样性的图像限制因素。这将促进对物质感知背后的计算过程的理解。它将在用于商业和日常应用的基于图像的模式识别算法方面创造高效的创新，例如新鲜农产品的质量分拣，以及监测潮湿天气下的司机表现以减少错误。

Humans visually perceive objects that vary in 3D shape, colour, sheen, and opacity, but understanding how this information is recovered from the images seen remains a mystery. The brain must exploit constraints on the way images are formed through the interaction of light with different surface properties. This project aims to identify image constraints that the brain measures to infer this material diversity. This will advance understandings of computational processes underlying material perception. It will create efficient innovations in image-based pattern recognition algorithms used for commercial and everyday applications, such as quality sorting of fresh produce, and monitoring driver performance in wet weather to reduce errors.