This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury website.

中文翻译：

---

在颜色，接近度和流量可靠性约束下使用迭代传播进行光流细化

这项研究提出了一种基于估计的可靠性图细化光流的策略。这些地图是首先估计的后验经过著名的Kanade-Lucas-Tomasi（KLT）的运动估计后。通过分别基于光流局部方差和KLT残差的时间演变的两个新定义的标准，然后在可靠性测度和颜色局部均匀性的控制下，通过两个阶段对运动图进行全局优化。根据在Middlebury数据集上进行的实验，作者的可靠性指标被证明是估计质量的良好指标。实际上，校正过程可以显着提高整体可靠性指标并减少整体误差。实验表明，该质量优于经典估计方法，在Middlebury网站上的排名为88/168。