Educational Supplement to Research Initiaion Award: MachineVision

研究启动奖的教育补充：机器视觉

• 批准号: 9109688
• 负责人: Shree Nayar
• 金额: $ 8万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-15 至 1994-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9109688&HistoricalAwards=false
• 关键词: Educational; Supplement; Research; Initiaion; Award

Points in scene, when illuminated, reflect light not only in the direction of the sensor but also between themselves. These interreflections can appreciably alter the appearance of the scene. Existing vision sensors and algorithms do not account for the effects of interreflections and hence often produce erroneous results. An initial investigation studied the effects of interreflections on shape information recovered by shape-from- intensity methods (e.g. photometric stereo). It was discovered that, for Lambertian surfaces, the erroneous (pseudo) shape recovered in the presence of interreflections is unique and can be mathematically related to the actual shape. An algorithm has been developed that uses a physical interreflection model to recover the actual shape of the surface from the psuedo shape. The research will be extended in the following directions. (1) The analysis of non-Lambertian (e.g. specular) interreflections. This entails the development of mathematical models for non-Lambertian interreflections as well as their application to the problem of recovering accurate shape in the presence of interreflections. (2) The detection and correction of errors produced by existing vision sensors (spot scanners, light stripe range finders, stereo systems) as a result of interreflections. For each sensor, a sensor model is used to obtain physical constraints that aid the analysis of interreflections. All analytical results will be verified by laboratory experiments. This research should achieve (a) fundamental advances in the analysis of interreflections and (b) substantial improvements in vision sensing technology.

场景中的点，当被照亮时，不仅在传感器的方向上反射光线，而且在它们之间也反射光线。这些相互反射可以明显地改变场景的外观。现有的视觉传感器和算法没有考虑到相互反射的影响，因此经常产生错误的结果。初步研究了相互反射对形状-强度法（如光度立体法）恢复的形状信息的影响。人们发现，对于朗伯曲面，在相互反射的存在下恢复的错误（伪）形状是唯一的，并且可以在数学上与实际形状相关。已经开发了一种算法，该算法使用物理互反射模型从伪形状中恢复表面的实际形状。本研究将向以下方向扩展。(1)非朗伯间反射（如镜面）分析。这就需要发展非朗伯间反射的数学模型，并将其应用于在间反射中恢复精确形状的问题。(2)现有视觉传感器（点扫描仪、光条测距仪、立体系统）由于互反射而产生的误差的检测和校正。对于每个传感器，使用传感器模型来获得有助于分析互反射的物理约束。所有分析结果将通过实验室实验进行验证。这项研究应该实现(a)相互反射分析的基本进展和(b)视觉传感技术的实质性改进。