CGV: Small: A Patch-based Framework for Capturing a World in Motion

CGV：小型：捕捉运动世界的基于补丁的框架

• 批准号: 1321168
• 负责人: Pradeep Sen
• 金额: $ 49.97万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321168&HistoricalAwards=false
• 关键词: CGV; Small; Patch; based; Framework

Although the real world is dynamic, many techniques used to image/capture it are fundamentally sequential in nature. For example, capturing a high-dynamic range (HDR) image of a scene (which contains a wide range of illumination) without special hardware involves taking a set of sequential images at different exposures, each one measuring a small range of illumination. However, this approach has problems when reconstructing the HDR image of dynamic scenes with moving subjects, since the individual frames are not registered correctly. Problems like this appear in many research areas, from medical imaging to computer vision.In this project, the PI and his team are developing a common framework that addresses artifacts from motion for a variety of different applications. Their key insight is that patch-based optimization can be used to handle motion inconsistencies without explicitly solving the challenging problem of accurate motion estimation. This produces results that are reconstructed from different inputs in a consistent manner without motion artifacts. The PI is exploring how this framework can be applied to several important research areas, from high-quality imaging to computer vision applications such as the reconstruction of dynamic scenes. Improved capture of dynamic scenes has the potential to transform the way certain imaging procedures (such as medical imaging) are done. Scientific results of this work are disseminated through technical publications at top venues in the graphics/vision communities, and the PI plans to release the algorithms developed online. Finally, this project involves high school and under-represented students into the research effort.

尽管现实世界是动态的，但许多用于拍摄/捕捉它的技术本质上是连续的。例如，在没有特殊硬件的情况下捕获场景(包含大范围照明)的高动态范围(HDR)图像涉及在不同曝光下拍摄一组连续图像，每个图像测量小范围的照明。然而，这种方法在重建具有运动对象的动态场景的HDR图像时存在问题，因为单个帧没有正确配准。这样的问题出现在许多研究领域，从医学成像到计算机视觉。在这个项目中，PI和他的团队正在开发一个公共框架，以解决各种不同应用程序中的运动伪像。他们的关键见解是，基于补丁的优化可以用于处理运动不一致，而不需要明确解决精确运动估计的挑战性问题。这会产生从不同输入以一致的方式重建的结果，而不会出现运动伪影。PI正在探索如何将这一框架应用于几个重要的研究领域，从高质量的成像到计算机视觉应用，如动态场景的重建。改进的动态场景捕捉有可能改变某些成像程序(如医学成像)的完成方式。这项工作的科学成果通过在图形/视觉社区的顶级场所的技术出版物传播，PI计划发布在线开发的算法。最后，这个项目让高中生和代表不足的学生参与到研究中来。