Current problems in lightness theory

亮度理论的当前问题

• 批准号: 1230793
• 负责人: Alan Gilchrist
• 金额: $ 35.85万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230793&HistoricalAwards=false
• 关键词: Current; problems; lightness; theory

The two leading theories of how the white/gray/black shade of a surface is computed in your brain will be tested against each other in a series of 10 experiments. Human observers will be presented with three-dimensional displays carefully constructed so that, under controlled viewing conditions, the two theories make different predictions regarding the gray shade that will be perceived for a test surface embedded within the display. All of the displays will incorporate a serious challenge for vision, namely, multiple areas of light and shadow. These displays will vary in terms of (a) whether the change of illumination is projected onto a surface or occurs at a corner (or bend) in the surface, (b) the number of different gray shades on each surface, and (c) the range of gray shades on a given surface. The observers will be asked to match critical parts of each display for gray level and level of illumination. The strengths and weaknesses of each of the theories will be evaluated based on the observer matches. Ideally these results will suggest how the respective strengths of the two theories might be integrated.How humans see the white, gray, and black shade of objects has not yet been explained scientifically. The basic problem is that the light that an object reflects to the eye does not reveal the shade of the object because it depends so heavily on the intensity of illumination on the object. Thus any intensity of light can be reflected from any shade of gray. The problem can only be solved by analyzing the surrounding context. Advances in our understanding of the nature of this analysis are absolutely necessary for guiding brain research in this area. The work will impact computer vision as well. At the moment, no robot can simply look at an object and report its shade of gray. Work with human vision provides the best hope for making better computer programs. Ironically, the proposed work will be conducted, not using computer images, as is almost universally done in human vision research, but with actual three-dimensional displays that are far more realistic.

关于表面的白色/灰色/黑色阴影如何在大脑中计算的两个主要理论将在一系列10个实验中进行相互测试。人类观察者将看到精心构造的三维显示器，以便在受控的观看条件下，两种理论对嵌入显示器内的测试表面所感知的灰色阴影做出不同的预测。所有的显示器都将对视觉提出严峻的挑战，即多个光影区域。这些显示将在以下方面变化：（a）照明的变化是投射到表面上还是发生在表面中的拐角（或弯曲）处，（B）每个表面上不同灰度的数目，以及（c）给定表面上灰度的范围。将要求观察员匹配每个显示器的关键部分的灰度级和照明水平。每个理论的优点和缺点将根据观察者匹配进行评估。理想情况下，这些结果将表明这两种理论各自的优势可能如何整合。人类如何看到物体的白色、灰色和黑色阴影尚未得到科学解释。基本的问题是，物体反射到眼睛的光不会显示物体的阴影，因为它严重依赖于物体上的照明强度。因此，任何强度的光都可以从任何灰度反射。这个问题只能通过分析周围的环境来解决。我们对这种分析性质的理解的进步对于指导这一领域的大脑研究是绝对必要的。这项工作也将影响计算机视觉。目前，没有机器人可以简单地看着物体并报告其灰色阴影。与人类视觉一起工作为制作更好的计算机程序提供了最好的希望。具有讽刺意味的是，拟议中的工作将进行，而不是使用计算机图像，因为几乎普遍在人类视觉研究，但与实际的三维显示，是更加现实。