NYI: Physical-Based Computer Vision: Polarization Vision, Reflectance Modeling and 3-D Vision

NYI：基于物理的计算机视觉：偏振视觉、反射建模和 3D 视觉

• 批准号: 9357757
• 负责人: Lawrence Wolff
• 金额: $ 31.25万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-07-15 至 2000-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9357757&HistoricalAwards=false
• 关键词: NYI; Physical; Based; Computer; Vision

Much of this research continues the development of the fundamentals of Polarization Vision for image understanding which the principal investigator initiated as a doctoral student. This research also continues the development of Polarization Cameras which are a new class of sensors that automatically sense polarization and compute either a visualization of polarization information, or, properties of a scene that are physically related to polarization. The additional physical dimensions of polarization of light beyond that of intensity, carry extra information from a scene that can provide a richer set of descriptive physical constraints for the understanding of images. Past work by the principal investigator has revealed Polarization Vision techniques that can be used to perform dielectric/metal material identification, specular and diffuse reflection component separation and analysis, as well as complex image segmentations that would be immensely more complicated or even infeasible using intensity and color information alone. A new intensity diffuse reflectance model has been formally developed for smooth dielectric surfaces that generalizes Lambert's Law. Implications of this more accurate diffuse reflectance model are being studied for 3-D shape recovery from reflectance in computer vision. New techniques are being developed for 3-D shape recovery from binocular stereo using correspondence of photometric values.

多 这项研究继续发展， 的 主要研究者在博士生时开始的用于图像理解的偏振视觉的基本原理。这项研究还继续偏振相机的发展，这是一类新的传感器，可以自动感测偏振并计算偏振信息的可视化，或者与偏振物理相关的场景属性。 除了强度之外，光的偏振的附加物理维度携带来自场景的额外信息，这些信息可以为理解图像提供更丰富的描述性物理约束。 首席研究员过去的工作揭示了偏振视觉技术，可用于执行电介质/金属材料 识别、镜面的 和 弥漫性 反射分量分离和分析，以及单独使用强度和颜色信息将非常复杂或甚至不可行的复杂图像分割。 一个新的强度漫反射模型已正式开发的光滑介质表面，推广朗伯定律。 这个更精确的漫反射模型的影响正在研究的3-D形状恢复从反射在计算机视觉。 三维形状恢复的新技术正在开发中 从 双目 立体声应用 对应 光度值。