Dept. of Electr. Eng. and Comput. Sci., Northwestern Univ., Evanston, IL.
IEEE Trans Image Process. 1993;2(3):341-52. doi: 10.1109/83.236533.
An adaptive regularized recursive displacement estimation algorithm is presented. An estimate of the displacement vector field (DVF) is obtained by minimizing the linearized displaced frame difference (DFD) using nu subsets (submasks) of a set of points that belong to a causal neighborhood (mask) around the working point. Assuming that the displacement vector is constant at all points inside the mask, nu systems of equations are formed based on the corresponding submasks. A set theoretic regularization approach is followed for solving this system of equations by using information about the noise and the solution. An expression for the variance of the linearization error is derived in quantifying the information about the noise. Prior information about the solution is incorporated into the algorithm using a causal oriented smoothness constraint (OSC) which also provides a spatially adaptive prediction model for the estimation DVF. It is shown that certain existing regularized recursive algorithms are special cases of the proposed algorithm, if a single mask is considered. Based on experiments with typical videoconferencing scenes, the improved performance of the proposed algorithm with respect to accuracy, robustness to occlusion and smoothness of the estimated DVF is demonstrated.
提出了一种自适应正则化递归位移估计算法。通过使用属于工作点周围因果邻域（掩模）的一组点的 nu 个子集（子掩模）来最小化线性化位移帧差（DFD），从而获得位移矢量场（DVF）的估计。假设在掩模内的所有点处位移矢量都是常数，基于相应的子掩模形成了 nu 个方程组。通过使用有关噪声和解决方案的信息，遵循集论正则化方法来求解该方程组。在量化有关噪声的信息时，推导出了线性化误差的方差表达式。通过使用面向因果的平滑约束（OSC）将关于解决方案的先验信息合并到算法中，该约束还为估计的 DVF 提供了空间自适应预测模型。如果仅考虑单个掩模，则表明某些现有的正则化递归算法是所提出算法的特例。通过对典型视频会议场景的实验，证明了所提出的算法在准确性、对遮挡的鲁棒性和估计的 DVF 的平滑性方面的性能得到了提高。
