Université de Bretagne Sud, Vannes, France.
IEEE Trans Image Process. 1998;7(5):703-19. doi: 10.1109/83.668027.
In this paper, we address the issue of recovering and segmenting the apparent velocity field in sequences of images. As for motion estimation, we minimize an objective function involving two robust terms. The first one cautiously captures the optical flow constraint, while the second (a priori) term incorporates a discontinuity-preserving smoothness constraint. To cope with the nonconvex minimization problem thus defined, we design an efficient deterministic multigrid procedure. It converges fast toward estimates of good quality, while revealing the large discontinuity structures of flow fields. We then propose an extension of the model by attaching to it a flexible object-based segmentation device based on deformable closed curves (different families of curve equipped with different kinds of prior can be easily supported). Experimental results on synthetic and natural sequences are presented, including an analysis of sensitivity to parameter tuning.
在本文中，我们解决了从图像序列中恢复和分割明显速度场的问题。对于运动估计，我们最小化了一个包含两个鲁棒项的目标函数。第一项谨慎地捕获了光流约束，而第二项（先验）项则包含了保持不连续性的平滑约束。为了解决由此定义的非凸最小化问题，我们设计了一种有效的确定性多重网格方法。它可以快速收敛到高质量的估计值，同时揭示出流场的大不连续性结构。然后，我们通过附加一个基于可变形封闭曲线的灵活的基于对象的分割设备（可以轻松支持不同类型的曲线族和不同类型的先验）来扩展该模型。我们在合成和自然序列上进行了实验，包括对参数调整的敏感性分析。
