光流
正规化(语言学)
离群值
不连续性分类
平滑度
计算机科学
人工智能
流量(数学)
算法
趋同(经济学)
数学优化
运动估计
数学
计算机视觉
图像(数学)
数学分析
经济增长
经济
几何学
作者
Jun Chen,Zemin Cai,Jianhuang Lai,Xiaohua Xie
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2018-03-01
卷期号:28 (3): 664-678
被引量:22
标识
DOI:10.1109/tcsvt.2016.2615324
摘要
Fast and accurate optical flow estimation is a challenging problem in computer vision. In this paper, we present a novel model to solve the optical flow problem by combining the strengths of the Split Bregman method with the advantages of an efficient variational framework. It allows us to employ different regularization tensors for the Split Bregman regularizer to preserve motion discontinuities. Simultaneously, a novel nonlocal Split Bregman method with adaptive support weights is also developed for the smoothness regularization to preserve motion details, repel outliers, enhance contrast, and reduce motion blurring and staircase effect. The proposed algorithm shows significant improvements in preserving sharp flow edges and important motion details. Most of all, it requires only a few iterations to achieve fast convergence and runs faster than the classic TV-L1 flow method. Our method significantly outperforms the current state-of-the-art methods on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.
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