Collaborative Research: Visual Information about surface curvature from patterns of image shading and contours

合作研究：从图像阴影和轮廓图案中获取有关表面曲率的视觉信息

• 批准号: 2238179
• 负责人: James Todd
• 金额: $ 28.72万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238179&HistoricalAwards=false
• 关键词: Collaborative; Research; Visual; Information; about

Our ability to perceive the 3D shapes of objects is generally taken for granted in our day-to-day experiences, but a detailed explanation of how that is possible has eluded researchers in both human and computer vision. Whereas objects are composed of tangible substances such as wood, glass or metal, our perceptions of them are based on patterns of light. Thus, an adequate theory of perception must be able to explain how patterns of light can uniquely specify the 3D structure of the visual environment. This research project will study the visual perception of object shape, with a focus on understanding how the human visual system processes and interprets information about the three-dimensional properties of objects. A better understanding of 3D vision could provide many practical applications. For example, it could lead to dramatic improvements in the design of visually guided robots or autonomous vehicles. It could also be important for building prosthetic devices for the blind to avoid potential obstacles during locomotion or to detect the presence of slippery surfaces that create a danger of falling. The computational analysis of 3D shape from shading has been studied for over 50 years but despite the broad interdisciplinary scope it has had relatively minimal success. One possible reason is that researchers have typically simplified the problem by making unrealistic assumptions about how light behaves in the natural environment. This makes it easier to derive mathematical solutions, but severely limits the generality of the resulting analyses. In the present work, the investigators will include many of the optical effects that have typically been ignored in this domain, including cast shadows, surface inter-reflections, anisotropic reflections, light transmission, and multiple patterns of illumination. They will also perform detailed measurements of the luminance field in each condition to discover properties that are relatively invariant over changes in scene parameters. The work will focus primarily on qualitative surface features, such as bumps, dimples, ridges, valleys, and saddles, and how they correspond to similar qualitative patterns of image shading. Numerous psychophysical experiments will be conducted to document 1) how observers’ judgments of 3D surface structure are influenced by the pattern of image shading; 2) the stability of these judgments over changes in surface materials and patterns of illumination; and 3) how smooth variations of light intensity on the eye provide information about 3D shape, even when all other aspects of shading have been removed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在我们的日常体验中，我们有能力认为对象的3D形状通常被认为是理所当然的，但是对这是可能的详细解释已经使人类和计算机视觉中的研究人员都避免了。尽管物体由木材，玻璃或金属等有形物质组成，但我们对它们的看法是基于光模式的。这是一个适当的感知理论必须能够解释光模式如何独特地指定视觉环境的3D结构。该研究项目将研究对象形状的视觉感知，重点是了解人类视觉系统如何处理和解释有关对象的三维特性的信息。更好地了解3D愿景可以提供许多实际应用。例如，这可能会导致视觉引导机器人或自动驾驶汽车的设计的显着改善。对于盲人制造假肢设备，以避免机车期间的潜在障碍或检测出造成跌落危险的湿滑表面的存在也可能很重要。从阴影中对3D形状的计算分析已经研究了50多年，但是目的地是它的跨学科范围相对较小。一个可能的原因是，研究人员通常通过对自然环境中的光的表现做出不切实际的假设来简化问题。这使得更容易得出数学解决方案，但严重限制了所得分析的一般性。在目前的工作中，研究人员将包含许多在该领域中通常被忽略的光学效应，包括铸造阴影，表面跨反射，各向异性反射，光传输和多种照明模式。他们还将在每个条件下对亮度场进行详细测量，以发现对场景参数变化相对不变的属性。这项工作将主要集中在定性的表面特征上，例如凸起，酒窝，山脊，山谷和马鞍，以及它们如何与类似的图像阴影相似的定性模式相对应。将进行许多心理物理实验，以记录1）观察者的3D表面结构法官如何受图像阴影模式的影响； 2）这些法官对表面材料和照明模式的变化的稳定； 3）眼睛上的光强度的平滑变化如何提供有关3D形状的信息，即使已经删除了阴影的所有其他方面。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响审查标准来通过评估来评估，并被认为是宝贵的支持。