報告內容：Adaptive Robust Principal Component Analysis: Principle and Applications in Computer Vision
報告人：伍世虔 教授
報告時間：11月28日 9:00
報告地點✬：現代交通工程中心7950會議室
報告人簡介:
伍世虔，南洋理工大學博士🙆♀️，湖北省“楚天學者”特聘教授,IEEE高級會員，中國高被引學者（2014-2018🕣，Elsevier）🏃🏻♀️。現任武漢科技大學特聘教授、博士生導師🙆🏽，智能信息處理與實時工業系統湖北省重點實驗室主任🤘，機器人與智能系統研究院副院長。曾任華中科技大學機械恒达先進製造技術研究所副所長🕰👨🏿🏫，新加坡國家科技局高級研究員（科學家）,多次擔任國際會議（ICICS、ICIEA☸️、ISITC、ICoIAS）等大會主席、程序委員會主席或分會主席。參與科研項目18項，其中主持11項🤱🏿，包括新加坡國家科技局、國家自然科學基金等課題🐦。出版專著二本，在國內外頂級期刊或會議發表學術論文200余篇，其中熱點文章2篇👮🏿♂️，高被引文章9篇，總引用超過5000次。主要研究方向有機器視覺🗃☛、模式識別🧑🏽🦲、機器學習及智能機器人等。
報告內容簡介:
Robust Principal Component Analysis (RPCA) by decomposing a matrix into low-rank plus sparse matrices offers a powerful tool in solving computer vision problems. However, the results by the existing RPCA-based methods are often unsatisfied in complex scenes, for example varying illumination, shape changes, occlusions and shadows etc. In this talk, an adaptive RPCA which simultaneously preserves low-rank structure and restores the corrupted parts is proposed. Specifically, the sum of weighted singular values is included in the objective function of minimization, and the weights are adaptively obtained by employing the proportion of information contained in corresponding singular values. Finally, we demonstrate several applications of the proposed methods in computer vision, such as image inpainting, shadow removal, background modeling and so on.
