Mitiche Amar, Sekkati Hicham
Institut National de la Recherche Scientifique, INRS-EMT, Montreal, Quebec, Canada.
IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1818-29. doi: 10.1109/TPAMI.2006.232.
This study investigates a variational, active curve evolution method for dense three-dimentional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation.
本研究探讨了一种变分主动曲线演化方法，用于对包含由可能移动的相机观察到的移动刚体的场景的图像序列中的密集三维（3D）分割和光流解释。该方法联合执行3D运动分割、3D解释（3D结构和运动的恢复）以及光流估计。目标泛函为每个分割区域包含两个数据项，一个基于将3D刚体运动的基本参数与光流相关联的仅运动方程，另一个基于Horn和Schunck光流约束。它还为每个区域包含两个正则化项，一个用于光流，另一个用于区域边界。泛函取最小值的必要条件通过水平集的主动曲线演化导致并发的3D运动分割，以及每个区域基本参数和光流的线性估计。随后，从估计的基本参数和光流中解析地恢复每个区域的3D运动螺旋和正则化相对深度。提供了验证该方法及其实现的示例。
