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Estimation of multiple motions: regularization and performance evaluation

机译:多个动作的估计:正则化和性能评估

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This paper deals with the problem of estimating multiple transparent motions that can occur in computer vision applications, e.g. in the case of semi-transparencies and occlusions, and also in medical imaging when different layers of tissue move independently. Methods based on the known optical-flow equation for two motions are extended in three ways. Firstly, we include a regularization term to cope with sparse flow fields. We obtain an Euler-Lagrange system of differential equations that becomes linear due to the use of the mixed motion parameters. The system of equations is solved for the mixed-motion parameters in analogy to the case of only one motion. To extract the motion parameters, the velocity vectors are treated as complex numbers and are obtained as the roots of a complex polynomial of a degree that is equal to the number of overlaid motions. Secondly, we extend a Fourier-Transform based method proposed by Vernon such as to obtain analytic solutions for more than two motions. Thirdly, we not only solve for the overlaid motions but also separate the moving layers. Performance is demonstrated by using synthetic and real sequences.
机译:本文讨论了估计在计算机视觉应用程序中可能发生的多个透明运动的问题。在半透明和闭塞的情况下,以及在医学成像中,当不同的组织层独立移动时。以三种方式扩展了基于已知光流方程的两个运动的方法。首先,我们包含一个正则化项以应对稀疏流场。我们获得了一个微分方程的Euler-Lagrange系统,由于使用混合运动参数,该系统变为线性。与仅一个运动的情况类似,求解混合运动参数的方程组。为了提取运动参数,将速度矢量视为复数,并将其作为与覆盖运动数相等的程度的复多项式的根。其次,我们扩展了由弗农提出的基于傅立叶变换的方法,以便获得两个以上运动的解析解。第三,我们不仅解决重叠运动,而且将运动层分开。通过使用合成序列和真实序列来演示性能。

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