题目：High-order Tensor Analysis for Groupwise Correspondence and its Applications in Computer Vision

报告人: Haibin Ling，Associate Professor, Temple University

报告时间：2015年5月21日星期四 15:30-17:30             

报告地点：中国科学院信息工程研究所3号楼（B2座）3224 室

Abstract：

Visual matching is a fundamental problem in computer vision (CV) and intensive research efforts have been devoted to building correspondence between a pair of visual objects. By contrast, finding correspondence among an ensemble of objects remains challenging. In this talk we will present a new unified framework for this problem and its application to computer vision and medical image. Specifically, we have established a close correlation between the classical multi-dimensional assignment (MDA) problem and low-rank tensor approximation. Such correlation paves a way of using high-order tensor analysis for groupwise visual matching that assumes an MDA formulation. Along the way, we developed a series of power-iteration algorithms and applied them to multiple target tracking (in the context of video surveillance) and deformable tracking (in the context of medical image analysis). I will also give an overview of research at my group.

Short Bio：

Haibin Ling received the B.S. degree in mathematics and the MS degree in computer science from Peking University, China, in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in Computer Science in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. After that, he joined Siemens Corporate Research as a research scientist. Since fall 2008, he has been with Temple University where he is now an Associate Professor in Computer and Information Sciences. Ling's research interests include computer vision, medical image analysis, human computer interaction, and machine learning. He received the Best Student Paper Award at the ACM Symposium on User Interface Software and Technology (UIST) in 2003, and the NSF CAREER Award in 2014. He has served as an Area Chair of CVPR 2014 and as a Guest Co-Editor for the Pattern Recognition in 2014.