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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >WHAT CAN BE SEEN IN A NOISY OPTICAL FLOW FIELD PROJECTED BY A MOVING PLANAR PATCH IN 3D SPACE
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WHAT CAN BE SEEN IN A NOISY OPTICAL FLOW FIELD PROJECTED BY A MOVING PLANAR PATCH IN 3D SPACE

机译:在3D空间中移动的平面斑块投影在嘈杂的光学流场中可以看到什么

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摘要

In this paper, we would like to propose a brand new interpretation to the so-called ''structure-from-motion'' (SFM) problem. The optical flow field projected by a moving rigid planar patch in 3D space is our main consideration. Instead of just obtaining an explicit 3D motion/pose solution like the old approaches did before, we focus our attention on analyzing its error sensitivity, uncertainty, and ambiguity from another point of view. Our new method can handle the above error analysis easily. As known well before, the optical flow field projected by a 3D moving planar patch can be completely expressed by eight coefficients (two for second-order, four for first-order, and two for zeroth-order). Based on these flow coefficients easily determined by a linear regression method or other similar approaches, the error sensitivity of 3D estimates can be analyzed quantitatively and qualitatively in a coarse-to-fine way. The concepts of camera fixation and singular value decomposition (SVD) play important roles in our analysis. There are three goals for our experiments: (1) To prove the correctness of the algorithms (simulated image). (2) To show the tendency of error sensitivity when the 3D poses of the target planar patch are varied in a controlled manner (simulated image). (3) To show that our analysis is workable in the real-world application (real-world image). (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd. [References: 28]
机译:在本文中,我们想对所谓的“运动结构”(SFM)问题提出全新的解释。我们主要考虑的是在3D空间中由移动的刚性平面斑块投射的光流场。我们不仅仅像以前的方法那样获得明确的3D运动/姿势解决方案,而是将注意力集中在从另一个角度分析其错误敏感性,不确定性和歧义性。我们的新方法可以轻松处理上述错误分析。众所周知,由3D移动平面贴片投影的光流场可以完全由八个系数表示(两个用于二阶,四个用于一阶,两个用于零阶)。基于这些可以通过线性回归方法或其他类似方法轻松确定的流量系数,可以从粗到精的方式定量和定性地分析3D估计值的误差敏感性。摄像机固定和奇异值分解(SVD)的概念在我们的分析中起着重要的作用。我们的实验有三个目标:(1)证明算法(模拟图像)的正确性。 (2)显示当目标平面贴片的3D姿态以受控方式变化时(模拟图像)的错误敏感性趋势。 (3)证明我们的分析在真实应用程序(真实世界图像)中是可行的。 (C)1997模式识别学会。由Elsevier Science Ltd.发布[参考:28]

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