Visual Perception and Cognition of Smoothly Curved Surfaces

本研究将通过以下方式探讨其基本机制： 人类观察者感知并记住的3D结构 光滑的曲面。 该项目将解决两个基本问题 问题：首先，三维空间中的物体是如何表示的 第二，光的性质是什么？ 结构决定了这种知识的感知。 从我们的知觉经验中可以清楚地看出， 视网膜提供了足够信息供我们感知 充分利用环境的3D结构;但它同样 仔细想想，视觉图像的特性 似乎与真实的 对象 真实的对象存在于3D空间中，并且由 有形的物质，如地球，金属，或肉，而 对象图像被限制到2D投影表面， 只不过是由闪烁的光组成的 尽管人类观察者如何处理 这种物体和图像之间看似不相称的映射 是一个古老的，在这方面的研究已被赋予了新的 近年来，通过尝试开发人工视觉 机器人系统和盲人假肢装置。 这 该项目将通过提供一个 更详细地了解如何解决类似的问题， 人类的视觉系统。 为了严格地模拟3D形式感知的过程， 首先必须准确地定义这些过程 为我们成就。 这一领域以往的大多数调查 假设每个可见表面点都被编码 在感知上，根据其相对于点的度量深度， 观察力 相反，这项研究旨在 探索更广泛的潜在表现形式。 它 基于早期的发现，即对度量的感知判断 深度令人惊讶地不准确，并且源于假设， 早期的结果支持，3D的视觉知识 结构通常涉及更抽象形式， 表示，其中观察到的表面被感知地 根据其非度量属性进行编码（例如，那些 涉及序数或名词关系）。 各种各样的实验，使用自然和计算机 生成的图像，将测试这一假设。 它们将需要 人们做出各种类型的判断，需要不同的 对3D结构的了解。 其他实验将 确定一些具体的计算机制，通过 3D形式的感知表示是从 光学信息，并测量这些 在广泛的观看条件下的机制。

This research will investigate the fundamental mechanisms by which human observers perceive and remember the 3D structures of smoothly curved surfaces. The project will address two basic questions: First, how are objects in 3-space represented mentally; and second, what are the properties of optical structure which determine this knowledge perceptually. It is clear from our perceptual experience that visual images on the retina provide sufficient information for us to perceive adequately the 3D structure of the environment; yet it is equally clear upon closer reflection that the properties of visual images seem to have little in common with the properties of real objects. Real objects exist in 3D space and are composed of tangible substances such as earth, metal, or flesh, while an image of an object is confined to a 2D projection surface and consists of nothing more than flickering patterns of light. Although the problem of how human observers are able to deal with this seemingly incommensurate mapping between objects and images is an ancient one, research in this area has been given a new impetus in recent years by attempts to develop artificial visual systems for robots and prosthetic devices for the blind. This project will attempt to facilitate these efforts by providing a more detailed understanding of how similar problems are solved by the human visual system. In order to model rigorously the processes of 3D form perception, it is first necessary to define precisely what those processes accomplish for us. Most previous investigations in this area have assumed that each visible surface point is encoded perceptually in terms of its metric depth relative to the point of observation. This research, in contrast, is designed to explore a much wider variety of potential representations. It builds on earlier findings that perceptual judgments of metric depth are surprisingly inaccurate and stems from the hypothesis, supported by the earlier results, that visual knowledge of 3D structure may often involve a more abstract form of representation in which an observed surface is perceptually encoded in terms of its nonmetric properties (e.g., those involving ordinal or nominal relations). A variety of experiments, using both natural and computer generated images, will test this hypothesis. They will require people to make various types of judgments requiring different levels of knowledge about 3D structure. Other experiments will identify some of the specific computational mechanisms through which perceptual representations of 3D form are generated from optical information, and measure the stability of these mechanisms over a wide range of viewing conditions.