Image constraints on the perception of surfaces and materials

人类在视觉上感知到的物体在3D形状、颜色、光泽和不透明度方面都有所不同，但如何从看到的图像中恢复这些信息仍然是一个谜。大脑必须利用不同表面特性的光相互作用形成图像的方式上的限制。该项目旨在确定大脑测量的图像约束，以推断这种物质多样性。这将促进对材料感知基础的计算过程的理解。它将在基于图像的模式识别算法方面进行高效创新，用于商业和日常应用，例如新鲜农产品的质量分类，以及在潮湿天气下监控驾驶员的表现以减少错误。

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.