A Correlation-Relaxation-Labeling Framework for Computing Optical Flow - Template Matching from a New Perspective
A Correlation-Relaxation-Labeling Framework for Computing Optical Flow - Template Matching from a New Perspective
复制标题
计算光流的相关松弛标记框架——新视角的模板匹配
DOI：
10.1109/34.406650
复制
发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Q.X. Wu
中科院分区：
文献类型：
--
作者：
Q.X. Wu
光流估计是基于比Horn和Schunk（1981）所暗示的更一般的时变图像模型来讨论的。重点是应用在低对比度图像，非刚性或不断发展的对象模式运动，以及大的帧间位移遇到。在处理这类应用时，模板匹配被认为比点对应和基于梯度的方法具有优势。讨论了特征匹配中的两个基本不确定性，即模板匹配和特征点对应。基于似然度量建立了相关模板匹配程序。将模板匹配与松弛标记相结合，提出了一种确定光流的方法。确定每个模板的候选位移数及其各自的似然度量。然后，通过要求运动域内的平滑度，使用松弛标记迭代更新每个候选的可能性。来自卫星的真实云图用于测试该方法。>
Optical flow estimation is discussed based on a model for time-varying images more general than that implied by Horn and Schunk (1981). The emphasis is on applications where low contrast imagery, nonrigid or evolving object patterns movement, as well as large interframe displacements are encountered. Template matching is identified as having advantages over point correspondence and the gradient-based approach in dealing with such applications. The two fundamental uncertainties in feature matching, whether template matching or feature point correspondences, are discussed. Correlation template matching procedures are established based on likelihood measurement. A method for determining optical flow is developed by combining template matching and relaxation labeling. A number of candidate displacements for each template and their respective likelihood measures are determined. Then, relaxation labeling is employed to iteratively update each candidate's likelihood by requiring smoothness within a motion field. Real cloud images from satellites are used to test the method. >