Human visual perception of shape, lightness, and lighting

人类对形状、亮度和照明的视觉感知

• 批准号: RGPIN-2016-05360
• 负责人: Murray, Richard
• 金额: $ 2.55万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709153
• 关键词: Human; visual; perception; shape; lightness

What could be easier than seeing? We see without trying, and usually without thinking about it. Seeing seems easy because our visual cortex is a powerful computing device with a lifetime of experience at seeing things. One reason why vision is a challenging computational task, though, is that all visual images are highly ambiguous, and any given image could conceivably be seen as depicting any of a wide range of shapes, colours, and lighting conditions. 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 in the scenes we see. For example, previous experiments have shown that starting from infancy, our visual system slowly learns that light usually shines from overhead, and our visual system uses this knowledge to perceive objects and scenes correctly. The main question addressed by the present research program 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 experimental tools called classification images, we will investigate what parts of visual images affect peoples' judgements about the brightness, orientation, or lighting conditions at a target location in an image. We will do this by introducing small random fluctuations into visual images, and over many thousands of presentations, measuring which random fluctuations affect peoples' judgements and which do not. We will use the results of these experiments to test computational theories of how people perceive shape, colour, and lighting. Second, we will build a new computational model that makes statistical assumptions about the shapes, colours, and lighting conditions in the scenes we see, and that uses these assumptions to interpret visual images. We will investigate what assumptions such a computational model must make, in order to interpret visual stimuli in ways that are as similar as possible to what human observers see. This research will help us understand normal human vision, both in day-to-day life and in more difficult tasks like operating in immersive environments or judging medical images. It will also provide information that will be useful for developing computer vision systems that see scenes and objects in the same way that people do.

有什么比看到容易的？ 我们看到不尝试，通常不考虑它。 看到看起来很容易，因为我们的Visual Cortex是一种强大的计算设备，在看到事物方面具有一生的经验。 视觉是一项具有挑战性的计算任务的原因之一是，所有视觉图像都是高度模棱两可的，并且可以想象的任何给定图像都可以看作描绘了各种形状，颜色和照明条件的任何范围。 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我们看到的场景中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开销中闪耀，并且我们的视觉系统使用这些知识正确感知对象和场景。 本研究计划提出的主要问题是，为了正确理解世界，人类视觉体系的其他关于现实世界形状，颜色和照明的其他事实是否是否依赖？ 我们将通过两种方式调查这个问题。 首先，使用称为分类图像的新型实验工具，我们将研究视觉图像的哪些部分会影响人们对图像中目标位置的亮度，方向或照明条件的判断。 我们将通过将小小的随机波动引入视觉图像，并在数千个演示中引入，测量随机波动会影响人们的判断，而这些波动不会影响人们的判断。 我们将使用这些实验的结果来测试人们如何感知形状，颜色和照明的计算理论。 其次，我们将建立一个新的计算模型，该模型在我们看到的场景中对形状，颜色和照明条件进行统计假设，并使用这些假设来解释视觉图像。 我们将研究这样的计算模型必须做出的假设，以便以与人类观察者所见的方式来解释视觉刺激。 这项研究将有助于我们在日常生活和更艰巨的任务中理解正常的人类视野，例如在身临其境的环境或评判医学图像。 它还将提供信息，这些信息对于开发以与人们相同的方式观察场景和对象的计算机视觉系统非常有用。