Optical Flow Estimation Based on Texture Constraints and Parametric Motion Model
基于纹理约束和参数化运动模型的光流估计

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.