Statistical properties of natural 3D scenes and their role in visual perception

自然 3D 场景的统计特性及其在视觉感知中的作用

• 批准号: 327528-2011
• 负责人: Murray, Richard
• 金额: $ 1.75万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=506298
• 关键词: Statistical; properties; natural; 3D; scenes

What could be easier than seeing? We see without trying, and usually without thinking about it. In fact, seeing is a remarkable feat. It seems easy because the part of our brain responsible for vision is a powerful computer with a lifetime of experience at seeing things. One reason why vision is challenging is that all visual images are highly ambiguous, and any given image could conceivably be seen as depicting a wide range of shapes, colours, and lights. Our visual system needs to overcome this ambiguity somehow, in order to perceive scenes and objects correctly. One way our visual system overcomes ambiguity is by relying on assumptions about what shapes, colours, and lights are most likely to occur. For example, previous experiments have shown that starting from infancy, our visual system slowly learns that light usually shines from above us, and our visual system uses this knowledge to perceive objects and scenes correctly. The main question addressed by the present research project is, what other facts about real-world shapes, colours, and lighting does the human visual system rely on in order to perceive the world correctly? We will investigate this question in two ways. First, using novel measurement tools, we will measure three perceptually important characteristics of the world: surface shape, surface colour, and illumination. We will do statistical analyses of these measurements, in order to see what reliable properties real-world shapes, colours, and lights have that the visual system can use in order to perceive scenes and objects correctly. Second, using perceptual experiments we will investigate what assumptions about shape, surface colour, and illumination guide human perception of scenes and objects. Observers will judge the shapes, colours, and lighting of carefully designed, computer-generated images of ambiguous objects, and using computational and mathematical models we will deduce what assumptions the visual system relies on to see scenes and objects correctly from these ambiguous images. This research will help us understand normal and abnormal human vision, and it will also be useful for designing computer vision systems that see scenes and objects correctly.

有什么比看到容易的？我们看到不尝试，通常不考虑它。实际上，看到是一项了不起的壮举。这似乎很容易，因为我们大脑负责视觉的部分是一台功能强大的计算机，拥有一生的观察经验。视觉具有挑战性的原因之一是所有视觉图像都是高度模棱两可的，并且任何给定的图像都可以看作是描绘了各种形状，颜色和灯光。我们的视觉系统需要以某种方式克服这种歧义，以正确地感知场景和对象。我们的视觉系统克服歧义的一种方法是依靠关于最有可能发生的形状，颜色和灯光的假设。例如，以前的实验表明，从婴儿期开始，我们的视觉系统慢慢地了解到光通常从我们上方发光，并且我们的视觉系统使用这些知识正确感知对象和场景。本研究项目提出的主要问题是，为了正确地感知世界，人类视觉系统的其他关于现实世界形状，颜色和照明的事实是否是否依赖？我们将通过两种方式调查这个问题。首先，使用新颖的测量工具，我们将测量世界上三个重要的重要特征：表面形状，表面色和照明。我们将对这些测量结果进行统计分析，以查看可靠的属性真实的形状，颜色和灯光具有视觉系统可以使用的，以正确地感知场景和对象。其次，使用感知实验，我们将研究形状，表面颜色和照明的假设指导人类对场景和物体的感知。观察者将判断精心设计的，计算机产生的模棱两可的对象的形状，颜色和照明，并使用计算和数学模型，我们将推论视觉系统依赖于从这些模棱两可的图像中正确观看场景和对象的假设。这项研究将有助于我们了解正常和异常的人类视力，并且对于设计正确的场景和对象的计算机视觉系统也将很有用。