Dept. of Electr. Eng., Florida Univ., Gainesville, FL.
IEEE Trans Image Process. 1995;4(3):346-57. doi: 10.1109/83.366482.
A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed.
提出了一类算法，用于从包含信号加噪声的两个连续图像帧估计位移矢量。在模型中，假设信号是非高斯的或（准平稳的）确定性的；并且，通过累积量估计器的一致性结果，作者统一了随机和确定性信号的观点。噪声源被假设为高斯（可能是空间和时间相关的）且协方差未知。将图像运动估计视为二维时滞估计问题，通过求解涉及三阶自累积量和互累积量的线性方程来估计移动物体的位移矢量。此外，开发了一种基于累积误差最优准则的块匹配算法。最后，使用最小化二维四阶累积量准则的递归算法估计每个像素的位移矢量。给出并讨论了仿真结果。
