报告题目 (Title)：双图正则化前景背景分离法
报告人 (Speaker)： 秦菁 教授（University of Kentucky）
报告时间 (Time)：2023年4月3日(周一) 13:00-14:30
报告地点 (Place)：校本部F309
邀请人(Inviter)：彭亚新 教授
主办部门：理学院数学系
报告摘要：Foreground-background separation (FBS) has been widely used in many applications, such as video surveillance and robotics. Due to the presence of the static background, a motion video can be decomposed into a low-rank background and a sparse foreground. Many regularization techniques that preserve low-rankness of matrices can therefore be imposed on the background. In the meanwhile, geometry-based regularizations, such as graph regularizations, can be imposed on the foreground. In this talk, I will present a dual-graph regularized FBS method based on weighted nuclear norm regularization and discuss its fast algorithm based on the matrix CUR decomposition. Numerical experiments on realistic human motion data sets are used to demonstrate the proposed effectiveness and robustness in separating moving objects from background, and the potential in robotic applications.
