运动场
光流
运动估计
滤波器(信号处理)
计算机科学
Curl(编程语言)
自适应滤波器
计算机视觉
数学
人工智能
核自适应滤波器
滤波器设计
算法
图像(数学)
程序设计语言
作者
Jun Chen,Yuanxin Song,Yuanxin Song,Zemin Cai,Guangguang Yang,Tianshu Liu
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-05-01
卷期号:33 (5): 2129-2146
被引量:2
标识
DOI:10.1109/tcsvt.2022.3219438
摘要
In this paper, we proposed a novel motion model with Helmholtz decomposition for complex fluid flows in a filtering-based optical flow framework, where the optimization of the regularization term is treated as a filtering process, and different motion patterns can be captured by finding appropriate filter kernels based on the designed filters. In this framework, we introduce a novel optical flow method with a joint spatial filter, which is based on the Helmholtz decomposition theorem that assumes a local motion field is composed of a curl field and a divergence field. By adjusting the scale of the weights in the filter kernels and combining a curl filter with a divergence filter in a certain ratio, it can simulate different motion patterns. In addition, if the correlation between the horizontal and vertical components of the optical flow field in the filter kernel is eliminated, it will be transformed into a linear motion model. Based on this linear motion model, we also develop a novel optical flow method with an adaptive guided filter. By finding an adaptive filter kernel driven by both the input image and the guided motion field for the designed filter, it can successfully capture different motion patterns, and yield an edge-preserving smoothing optical flow field. Most importantly, the proposed optical flow model provides a new way to design the regularizers for capturing different motion patterns in complex fluid flow. In particular, the designed optical flow method with an adaptive guided filter significantly outperforms the current state-of-the-art optical flow methods in predicting complex fluid flows.
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