题目：Dual-Graph Regularized Foreground Background Separation
报告人：秦菁
讲座时间：2023年3月27日（星期一），13：30
讲座地点：综合楼615会议室
报告人简介：
秦菁，美国肯塔基大学助理教授。研究方向包括图像处理，最优化，基于图论的数据分析，和高维线性代数及其应用。研究成果发表在SIAM Journal on Imaging Sciences, Journal of Scientific Computing, Inverse Problems and Imaging, IEEE Transactions on Geoscience and Remote Sensing 等期刊，和IEEE IGARSS, ISBI, EMBC, ICMA等相关会议。
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.
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