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

机译:一种从双帧光流量获得结构的分层方法

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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, tile 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:提出了一种分层迭代算法,用于从两个帧光流中提取结构。该算法利用两个事实:一个是在许多应用中,如面部和手势识别,与光学中心和物体之间的距离相比,场景中的物体的可见表面的瓷砖深度变化很小;另一种是,对于任何双帧光学流程估计,在粗水平下减轻了时叠种问题,使得估计趋于更准确。导出光流,深度和运动参数之间的关系的分层表示,并且通过两个线性子系统迭代地解决了所得到的非线性系统。在驯型水平处,物体的表面趋于平坦,使得逆深度趋于是恒定的,其用作初始深度图。在每个级别的两个线性子系统估计逆深度和运动参数,结果传播到更精细的水平。使用计算机呈现图像和真实图像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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