提出了一种基于局部小平面运动的光流估计新方法。目的是获得精确致密的光流估计结果。与以往采用亮度一致性区域作为假设平面的算法不同，本算法利用序列图像的纹理信息，在纹理分割区域的基础上，进行运动估计。该算法首先通过微分法计算粗光流，可以得到参数化光流模型的初始估计，然后通过区域迭代算法，调整初始估计，从而得到精细的平面分割及其对应的参数化光流模型。基于纹理信息的部分拟合算法被用于算法的每一步当中，保证了纹理边缘位置的光流估计值的准确性。实验采用了标准图像序列，结果表明，可以得到更为精细的光流估计结果，特别是对于那些有着丰富纹理信息的室外环境的图像序列，而且在运动边界处的结果改善尤为明显。

This paper presents a new method for estimating dense and accurate optical flow based on the motion of planar regions. Different from previous approach, textural information are exploited to organize and constrain the interpretation of the motion. Regions with similar texture properties are supposed to be planar patches. This hypothesis is more reasonable and effective than those hypotheses using the brightness smooth region as planar patches. Parametric flow models are estimated in these textually consistent regions in two steps that first compute a coarse fit according to the coarse optical flow. Then this initial fit is refined using a generalization of the standard area-based regression approaches. Partial fit based on the texture information is employed all through the process. Accurate optical flows are computed finally according to the refined parametric motion models. Experimental results on a variety of images sequence indicate that our method produce accurate flow estimates especially in outdoor environment containing rich texture content. Furthermore the incorporation of the textural information through partial fit and image segmentation provides precise localization of motion boundaries and makes this method overmatch the previous brightness smooth hypothesis based approaches.