Sparse Representation and Low Rank Methods for Image Restoration and Classification

时间:2013-12-26 点击:

报告题目:Sparse Representation and Low Rank Methods for Image Restoration and Classification

报告时间:12月26日 10:00

报告地点:海韵行政楼C505

报告人:Lei Zhang

报告人简介

Lei Zhang received the B.Sc. degree in 1995 from Shenyang Institute of Aeronautical Engineering, Shenyang, P.R. China, the M.Sc. and Ph.D degrees in Control Theory and Engineering from Northwestern Polytechnical University, Xi'an, P.R. China, respectively in 1998 and 2001. From 2001 to 2002, he was a research associate in the Dept. of Computing, The Hong Kong Polytechnic University. From Jan. 2003 to Jan. 2006 he worked as a Postdoctoral Fellow in the Dept. of Electrical and Computer Engineering, McMaster University, Canada. In 2006, he joined the Dept. of Computing, The Hong Kong Polytechnic University, as an Assistant Professor. Since Sept. 2010, he has been an Associate Professor in the same department. His research interests include Image and Video Processing, Computer Vision, Pattern Recognition and Biometrics, etc. Dr. Zhang has published about 200 papers in those areas. Dr. Zhang is currently an Associate Editor of IEEE Trans. on CSVT and Image and Vision Computing. He was awarded the 2012-13 Faculty Award in Research and Scholarly Activities. More information can be found in his homepage http://www4.comp.polyu.edu.hk/~cslzhang/.


讲座概要

Sparse representation and low rank techniques have shown promising results in image processing and computer vision. In this talk we will introduce some of our recent research progress in sparse representation and low rank based image restoration and image classification. For image restoration, we will introduce the nonlocally centralized sparse representation (NCSR) model and the weighted nuclear norm minimization (WNNM) model we recently developed. Sparse representation has also been attracting lots of attention in image classification tasks such as face recognition. However, is it really the sparsity that helps face recognition? In this talk, we will show our recent findings along this line.