Mechanisms for the Perception of Surface Qualities

即使是最好的计算机视觉系统也远远达不到人类的视觉。一个人可以毫不费力地辨认出金属是金属，或者木头是木头。这些是材质感知的示例，其中视觉依赖于颜色、纹理、透明度和光泽度的组合来识别对象的材质表面。在美国国家科学基金会的支持下，爱德华·阿德尔森博士研究了知觉如何将信息组合在一起，从而使物质知觉成为可能。他的工作假设是，人类的视觉依赖于图像中颜色和图案之间的某些统计关系，并使用这些关系来推断材料。然而，这些关系的确切性质仍有待了解。这项研究的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，以及他们吃的食物。如果我们能够理解决定材料知觉的原理，这将有助于寻求制造表面外观改善的新产品的工业研究人员，例如新型化妆品或油漆。对物质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，自动车辆应该能够区分路面、泥土、泥土或冰，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。通过模仿人类的视觉机制，我们可以开发出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.