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A hierarchical approach for obtaining structure from two-frame optical flow

机译:从两帧光流中获取结构的分层方法

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A hierarchical iterative algorithm is proposed for extracting structure from two-frame optical flow. The algorithm exploits two facts: one is that in many applications, such as face and gesture recognition, the depth variation of the visible surface of an object in a scene is small compared to the distance between the optical center and the object; the other is that the time aliasing problem is alleviated at the coarse level for any two-frame optical flow estimate so that the estimate tends to be more accurate. A hierarchical representation for the relationship between the optical flow, depth, and the motion parameters is derived, and the resulting non-linear system is iteratively solved through two linear subsystems. At the coarsest level, the surface of the object tends to be flat, so that the inverse depth tends to be a constant, which is used as the initial depth map. Inverse depth and motion parameters are estimated by the two linear subsystems at each level and the results are propagated to finer levels. Error analysis and experiments using both computer-rendered images and real images demonstrate the correctness and effectiveness of our algorithm.
机译:提出了一种层次迭代算法,用于从两帧光流中提取结构。该算法利用了两个事实:一是在许多应用中,例如面部和手势识别,场景中物体可见表面的深度变化比光学中心与物体之间的距离小。另一个问题是,对于任何两帧光流估计,都可以在粗略的水平上缓解时间混叠问题,从而使估计趋于更加准确。推导了光流,深度和运动参数之间关系的层次表示,并通过两个线性子系统迭代求解了所得的非线性系统。在最粗糙的水平上,物体的表面趋于平坦,因此反深度趋于恒定,这被用作初始深度图。逆深度和运动参数由每个级别的两个线性子系统估计,并且结果传播到更精细的级别。使用计算机渲染图像和真实图像进行的错误分析和实验证明了我们算法的正确性和有效性。

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