IBM Thomas J. Watson Res. Center, Yorktown Heights, NY.
IEEE Trans Image Process. 1997;6(11):1503-16. doi: 10.1109/83.641411.
We propose a model for the interframe correspondences existing between pixels of an image sequence. These correspondences form the elements of a field called the motion field. In our model, spatial neighborhoods of motion elements are related based on a generalization of autoregressive (AR) modeling of the time-series. We also propose a joint spatio-temporal model by including spatial neighborhoods of pixel intensities in the motion model. A fundamental difference of our approach with most previous approaches to modeling motion is in basing our model on concepts from statistical signal processing. The developments in this paper give rise to the promise of extending well-understood tools of signal processing (e.g., filtering) to the analysis and processing of motion fields. Simulation results presented show the performance of our models in interframe prediction; specifically, on average the motion model performs 29% better in terms of the mean squared error energy over a commonly used pel-recursive approach. The spatio-temporal model improves the prediction efficiencies by 8% over the motion model. Our model can also be used to obtain estimates of the optical flow field as the simulations demonstrate.
我们提出了一种用于图像序列中像素之间的帧间对应关系的模型。这些对应关系构成了一个称为运动场的场的元素。在我们的模型中，运动元素的空间邻域是基于时间序列的自回归 (AR) 建模的推广来相关的。我们还通过在运动模型中包含像素强度的空间邻域来提出联合时空模型。与之前大多数建模运动的方法相比，我们的方法的一个基本区别在于，我们的模型基于统计信号处理的概念。本文的发展为将信号处理（例如滤波）中众所周知的工具扩展到运动场的分析和处理带来了希望。所呈现的仿真结果展示了我们的模型在帧间预测中的性能；具体来说，平均而言，运动模型在均方误差能量方面比常用的像素递归方法好 29%。时空模型比运动模型提高了 8%的预测效率。我们的模型还可以用于获得光流场的估计，如模拟所示。
