Human visual perception of shape, lightness, and lighting

还有什么比看到更容易的呢？我们不看，通常也不去想。看东西似乎很容易，因为我们的视觉皮层是一个强大的计算设备，拥有一生的视觉经验。然而，视觉是一项具有挑战性的计算任务的一个原因是，所有的视觉图像都是高度模糊的，任何给定的图像都可能被视为描绘了各种形状、颜色和照明条件中的任何一种。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我们所看到的场景中最有可能出现的形状、颜色和光线的假设。例如，先前的实验表明，从婴儿时期开始，我们的视觉系统就慢慢地认识到光线通常是从头顶照射过来的，我们的视觉系统利用这一知识来正确地感知物体和场景。目前的研究计划所解决的主要问题是，人类视觉系统为了正确地感知世界，还依赖于关于现实世界的形状、颜色和光线的哪些其他事实？我们将从两个方面来研究这个问题。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。我们将通过在视觉图像中引入小的随机波动，并在成千上万的演示中，测量哪些随机波动会影响人们的判断，哪些不会。我们将使用这些实验的结果来测试人们如何感知形状、颜色和光线的计算理论。其次，我们将建立一个新的计算模型，对我们看到的场景中的形状、颜色和照明条件做出统计假设，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什么样的假设，以便以尽可能与人类观察者所看到的相似的方式解释视觉刺激。这项研究将帮助我们理解正常的人类视觉，无论是在日常生活中，还是在更困难的任务中，如在沉浸式环境中操作或判断医学图像。它还将为开发像人类一样看到场景和物体的计算机视觉系统提供有用的信息。

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.