The Measurement and Interpretation of 2D and 3D Image Motion

2D 和 3D 图像运动的测量和解释

• 批准号: 41810-2012
• 负责人: Barron, John
• 金额: $ 1.02万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568861
• 关键词: Measurement; Interpretation; 2D; 3D; Image

John Barron (and his colleagues and students) are principally interested in measuring 2D and 3D image motion (the variants are called optical flow, scene flow and range flow) in 2D and 3D monocular and binocular image sequences. Some issues they are addressing include: (1) Image noise is a continuing source of error in any flow calculation. We are devising theoretical and practical ways to measure local (Gaussian) image noise and using this information in flow calculations to increase the accuracy of computed flow while maintaining its density. (2) Discontinuities in 2D and 3D optical flow fields caused by occlusion are currently handled poorly by state-of-the-art optical flow algorithms and yet this type of flow is fundamental in human vision. We are designing new occlusion boundary-based optical flow algorithms, which will yield good quantitative and qualitative flow at such boundaries. (3) We are measuring scene and range flow in the synthetic Auckland and real Daimler stereo driving sequences using scene flow and range flow. Scene flow uses stereo disparity maps (and its gradient field) and left and right image optical flow while range flow uses stereo depth maps and their spatio-temporal derivative maps. Both provide estimates of 3D camera motion at each visible scene point. Which is best and why? The answer will lead to better (hybrid) scene/range flow algorithms. Scene/range flow will also greatly benefit from our image noise and occlusion work. Our ultimate goal is to produce robust, accurate and fast scene/range flow algorithms. We plan to validate them using the real-time stereo depth maps produced from Steven Beauchemin's ``Roadlab'' car (outfitted with stereo cameras and a state-of-the-art multiprocessor computer). Our work will greatly enhance Computer Vision based driving aids.

John Barron(和他的同事和学生)主要对测量2D和3D单目和双目图像序列中的2D和3D图像运动(变种称为光流、场景流和距离流)感兴趣。他们正在解决的一些问题包括： (1)图像噪声是任何流量计算中的持续误差来源。我们正在设计理论和实用的方法来测量局部(高斯)图像噪声，并在流动计算中使用这些信息来提高计算流动的精度，同时保持其密度。 (2)目前最先进的光流算法很难处理由遮挡引起的2D和3D光流场中的不连续性，但这种类型的流在人类视觉中是基本的。我们正在设计新的基于遮挡边界的光流算法，它将在这样的边界上产生良好的定量和定性流动。 (3)我们使用场景流和距离流来测量合成奥克兰和真实戴姆勒立体驾驶序列中的场景流和射程流。场景流使用立体视差图(及其梯度场)和左右图像光流，而距离流使用立体深度图及其时空导数图。两者都提供了对每个可见场景点处的3D摄影机运动的估计。哪一个是最好的？为什么？答案将导致更好的(混合)场景/范围流算法。场景/范围流也将极大地受益于我们的图像噪声和遮挡工作。 我们的最终目标是产生健壮、准确和快速的场景/距离流算法。我们计划用史蒂文·比彻明的“RoadLab”汽车(配备了立体摄像机和最先进的多处理器计算机)制作的实时立体深度图来验证它们。我们的工作将极大地提高基于计算机视觉的驾驶辅助设备。