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.

我们感知物体3D形状的能力在我们的日常经验中通常被认为是理所当然的，但人类和计算机视觉研究人员都没有详细解释这是如何实现的。虽然物体是由有形物质组成的，如木材，玻璃或金属，但我们对它们的感知是基于光的模式。因此，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。该研究项目将研究物体形状的视觉感知，重点是了解人类视觉系统如何处理和解释有关物体三维属性的信息。更好地理解3D视觉可以提供许多实际应用。例如，它可能会导致视觉引导机器人或自动驾驶汽车的设计得到显着改进。这对于为盲人建造假肢装置也很重要，可以在运动过程中避开潜在的障碍物，或者检测是否存在会造成跌倒危险的光滑表面。 从阴影计算分析3D形状已经研究了50多年，但尽管广泛的跨学科范围，它已经取得了相对较小的成功。一个可能的原因是，研究人员通常通过对自然环境中光的行为做出不切实际的假设来简化问题。这使得它更容易获得数学解决方案，但严重限制了所得到的分析的一般性。在目前的工作中，研究人员将包括许多在这一领域通常被忽略的光学效应，包括投射阴影，表面相互反射，各向异性反射，光透射和多种照明模式。他们还将对每种条件下的亮度场进行详细测量，以发现随着场景参数变化而相对不变的属性。这项工作将主要集中在定性的表面特征，如凹凸，酒窝，脊，谷，鞍，以及它们如何对应于类似的定性模式的图像阴影。将进行大量的心理物理实验，以证明1）观察者对3D表面结构的判断如何受到图像阴影模式的影响; 2）这些判断对表面材料和照明模式变化的稳定性;以及3）眼睛上光强度的平滑变化如何提供关于3D形状的信息，这个奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。