SGER: Depth from Physics in Computer Vision

SGER：计算机视觉物理学的深度

• 批准号: 9526210
• 负责人: Murray Loew
• 金额: $ 5万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-15 至 1997-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9526210&HistoricalAwards=false
• 关键词: SGER; Depth; Physics; Computer; Vision

This award, in the Small Grants for Exploratory Research mode, will examine and test a first-principles approach for determining the relative depths of objects in images using only knowledge about the medium and the intensities acquired in a single view. Most methods for inferring depth (or range) in computer vision have depended on the use of multiple cameras and normally require the identification of corresponding points in the resulting images. Alternatively, rangefinder methods depend on sharpness-of-focus measures that require calibrated-focus lenses and an unambiguous definition of focus. The planned approach is based on the physics of light transmission and attenuation in a translucent diffuse medium. These investigators have shown, in theory and with a small set of experiments (in simulated biological tissue), that it is straightforward to infer the relative depths of structures when they are embedded in such a medium. This work is particularly exploratory because the original results arose as a byproduct of other research and so the extent of its applicability in a more general setting is not known. Two of the questions to be probed are: what are the limitations (e.g., minimum detectable depth difference) and sensitivities (to assumptions, and to changes in parameters) of the model; and what is the effect of the size and shape of the object on determination of its relative depth. The research is aimed at the development of a capability to identify unambiguously the relative depths of structures in an image, and thus to prevent confusion about which structures overlie which others. Because a single view is sufficient, it will be possible to make this identification retrospectively, as long as the properties of the medium are known. This should be useful in evaluating images arising, for example, in underwater, biological, and atmospheric applications, as well as in more-turbid environments.

这个奖项，在探索性研究模式的小赠款，将检查和测试的第一原理方法，用于确定图像中的对象的相对深度，只使用有关的介质和在一个单一的视图中获得的强度的知识。 在计算机视觉中，大多数用于推断深度（或范围）的方法都依赖于使用多个相机，并且通常需要识别结果图像中的对应点。另外，测距仪方法依赖于需要校准焦距镜头和明确定义焦点的聚焦锐度测量。 计划中的方法是基于半透明漫射介质中的光传输和衰减的物理学。 这些研究人员在理论上和一小部分实验（在模拟生物组织中）表明，当结构嵌入这种介质时，可以直接推断结构的相对深度。 这项工作特别具有探索性，因为最初的结果是作为其他研究的副产品出现的，因此其在更一般环境中的适用性程度尚不清楚。 需要探讨的两个问题是：限制是什么（例如，最小可探测深度差）和模型的敏感性（对假设和参数变化的敏感性）;以及物体的大小和形状对确定其相对深度有何影响。 这项研究的目的是发展一种能力，以明确地识别图像中结构的相对深度，从而防止混淆哪些结构覆盖哪些其他结构。因为一个单一的视图是足够的，它将有可能作出这种识别回顾，只要介质的属性是已知的。 这应该是有用的，在评估产生的图像，例如，在水下，生物和大气应用，以及在更浑浊的环境。