Joint optical flow estimation, segmentation, and 3D interpretation with level sets
Joint optical flow estimation, segmentation, and 3D interpretation with level sets
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DOI：
10.1016/j.cviu.2005.11.002
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发表时间：
2006-08-01
影响因子：
4.5
通讯作者：
Mitiche, A.
中科院分区：
文献类型：
--
作者：
Sekkati, H.;Mitiche, A.
本文描述了一种具有主动曲线演化和水平集的变分方法，用于对空间中独立移动的刚性物体产生的光流进行估计、分割和 3D 解释。估计、分割和 3D 解释是联合执行的。分割基于与每个分割区域中的单个刚性运动一致的光流估计。该方法允许观察系统和观察对象移动，导致迭代三个步骤直至收敛：（a）通过水平集演化闭合曲线，以及在分割的每个区域中，（b）刚性运动基本参数的线性最小二乘计算，（c）与单个刚性运动一致的光流估计。根据估计的基本参数和光流，对分割的每个区域分析恢复刚性运动的平移和旋转分量以及正则化相对深度。提供了几个真实图像序列的例子，验证了该方法的有效性。 (c) 2005 Elsevier Inc. 保留所有权利。
This paper describes a variational method with active curve evolution and level sets for the estimation, segmentation, and 3D interpretation of optical flow generated by independently moving rigid objects in space. Estimation, segmentation, and 3D interpretation are performed jointly. Segmentation is based on an estimate of optical flow consistent with a single rigid motion in each segmentation region. The method, which allows both viewing system and viewed objects to move, results in three steps iterated until convergence: (a) evolution of closed curves via level sets and, in each region of the segmentation, (b) linear least squares computation of the essential parameters of rigid motion, (c) estimation of optical flow consistent with a single rigid motion. The translational and rotational components of rigid motion and regularized relative depth are recovered analytically for each region of the segmentation from the estimated essential parameters and optical flow. Several examples with real image sequences are provided which verify the validity of the method. (c) 2005 Elsevier Inc. All rights reserved.