Regularized Pel-Recursive Motion Estimation Using Generalized Cross-Validation and Spatial Adaptation
This work addresses motion estimation in computer vision, particularly for handling complex scenarios like occlusions, but appears incremental as it builds on existing pel-recursive and regularization frameworks.
The authors tackled the problem of estimating 2-D optical flow using regularized pel-recursive algorithms, addressing issues like outliers and motion discontinuities by proposing a method that uses Generalized Cross-Validation and spatial adaptation to estimate a per-pixel regularization matrix, resulting in robust optical flow estimates as indicated by preliminary experiments.
The computation of 2-D optical flow by means of regularized pel-recursive algorithms raises a host of issues, which include the treatment of outliers, motion discontinuities and occlusion among other problems. We propose a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a technique called Generalized Cross-Validation to estimate the best regularization scheme for a given pixel. In our model, the regularization parameter is a matrix whose entries can account for diverse sources of error. The estimation of the motion vectors takes into consideration local properties of the image following a spatially adaptive approach where each moving pixel is supposed to have its own regularization matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.