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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=549111
• 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.

还有什么比看更容易的呢？我们不用去尝试就能看见，而且通常不用去思考。事实上，看见是一项了不起的壮举。这看起来很容易，因为我们大脑中负责视觉的部分是一台功能强大的计算机，拥有一生的观察事物的经验。视觉之所以具有挑战性的一个原因是，所有的视觉图像都是高度模糊的，任何给定的图像都可以被视为描绘了广泛的形状，颜色和光线。我们的视觉系统需要以某种方式克服这种模糊性，以便正确地感知场景和物体。我们的视觉系统克服模糊性的一种方法是依赖于对最有可能出现的形状，颜色和光线的假设。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学习光线通常从我们上方照射，我们的视觉系统使用这种知识来正确地感知物体和场景。本研究项目解决的主要问题是，人类视觉系统依赖于真实世界的形状，颜色和照明的其他事实来正确感知世界？我们将从两个方面来研究这个问题。首先，使用新的测量工具，我们将测量世界的三个感知重要特征：表面形状，表面颜色和照明。我们将对这些测量结果进行统计分析，以了解真实世界的形状，颜色和光线具有哪些可靠的特性，视觉系统可以使用这些特性来正确感知场景和物体。其次，使用知觉实验，我们将调查什么样的假设形状，表面颜色和照明引导人类感知的场景和物体。观察者将判断形状，颜色和照明的精心设计，计算机生成的图像模糊的对象，并使用计算和数学模型，我们将推断出什么样的假设，视觉系统依赖于正确地看到这些模糊的图像的场景和对象。这项研究将帮助我们了解正常和异常的人类视觉，也将有助于设计正确看到场景和物体的计算机视觉系统。