Corp. Res. and Dev. Robert Bosch GmbH, Hildesheim.
IEEE Trans Image Process. 1997;6(2):234-50. doi: 10.1109/83.551695.
Motion estimation belongs to key techniques in image sequence processing. Segmentation of the motion fields such that, ideally, each independently moving object uniquely corresponds to one region, is one of the essential elements in object-based image processing. This paper is concerned with unsupervised simultaneous estimation of dense motion fields and their segmentations. It is based on a stochastic model relating image intensities to motion information. Based on the analysis of natural images, a region-based model of motion-compensated prediction error is proposed. In each region the error is modeled by a white stationary generalized Gaussian random process. The motion field and its segmentation are themselves modeled by a compound Gibbs/Markov random field accounting for statistical bindings in spatial direction and along the direction of motion trajectories. The a posteriori distribution of the motion field for a given image sequence is formulated as an objective function, such that its maximization results in the MAP estimate. A deterministic multiscale relaxation technique with regular structure is employed for optimization of the objective function. Simulation results are in a good agreement with human perception for both the motion fields and their segmentations.
运动估计属于图像序列处理的关键技术。将运动域分割成这样的区域，即每个独立运动的物体唯一对应一个区域，是基于对象的图像处理的基本要素之一。本文关注的是密集运动场及其分割的无监督同时估计。它基于将图像强度与运动信息相关联的随机模型。基于对自然图像的分析，提出了一种基于区域的运动补偿预测误差模型。在每个区域中，误差由一个白色平稳广义高斯随机过程建模。运动场及其分割本身由一个复合的 Gibbs/Markov 随机场建模，该随机场考虑了空间方向和运动轨迹方向上的统计绑定。给定图像序列的运动场的后验分布被公式化为一个目标函数，使得其最大化结果是 MAP 估计。确定性多尺度松弛技术具有正则结构，用于优化目标函数。对于运动场及其分割，模拟结果与人类感知具有良好的一致性。
