Mechanisms for the Perception of Surface Qualities

即使是最好的计算机视觉系统福尔斯也远不及人类的视觉。 一个人没有问题识别金属作为金属或木材作为木材。 这些是材料感知的例子，其中视觉依赖于颜色，纹理，透明度和光泽度的组合来识别物体的材料表面。 在NSF的支持下，Edward Adelson博士研究了感知如何将信息放在一起，从而允许材料感知。 他的工作假设是，人类视觉依赖于图像中颜色和图案之间的某些统计关系，并利用这些关系来推断材料。 然而，这些关系的确切性质仍有待理解。研究的更广泛影响在日常生活中很重要。 人们非常关心他们的皮肤和头发的外观，他们穿的衣服，他们吃的食物。 如果我们能够理解决定材料感知的原理，这将有助于工业研究人员寻求制造具有改进表面外观的新产品，例如新型化妆品或涂料。 对材料感知的理解也可能在更好的机器视觉系统中得到回报。 例如，自动驾驶汽车应该能够区分路面、泥土、泥或冰，并相应地调整其驾驶，但今天的机器视觉系统发现这些问题相当困难。 通过模仿人类视觉的机制，我们可以开发更强大的机器视觉系统。

Even the best computer vision system falls far short of human vision. A person has no problem recognizing metal as metal or wood as wood. These are examples of material perception in which vision relies on combinations of color, texture, transparency, and glossiness to recognize the material surface of objects. With NSF Support Dr. Edward Adelson studies how perception puts information together, thereby allowing material perception. His working hypothesis is that human vision relies on certain statistical relationships between colors and patterns within an image, and uses these to infer the material. However, the exact nature of these relationships remains to be understood.Broader impacts of the research are important in everyday life. People care greatly about the appearance of their skin and hair, the clothing they wear, and the food they eat. If we can understand the principles that determine material perception, it will help industrial researchers who seek to make new products with improved surface appearance, such as new kinds of cosmetics or paint. An understanding of material perception may also pay off in better machine vision systems. For instance, an automated vehicle should be able to distinguish pavement, dirt, mud, or ice and adjust its driving accordingly, but today's machine vision systems find such problems quite difficult. By emulating the mechanisms of human vision, we can develop more powerful machine vision systems.