关于Prof. Xin Li学术报告的通知

时间:2017-05-08浏览:144设置

 关于Prof. Xin Li学术报告的通知

 

TopicReliable Geometric Data Mapping and its Engineering and Graphics Applications

Time2017510日(周三)下午2:00 – 4:00

Venue:玉泉校区教一103会议室

SpeakerProf. Xin Li, Louisiana State University, US

 

Abstract:

Geometric data mapping is to establish correspondence between spatiotemporal geometric data. Its computation is a fundamental problem closely related to registration, integration, or comparison of images or geometric models in computer vision and graphics, and therefore has many practical applications. I will talk about different types of geometric mapping problems, from full-body cross-object mapping to partial matching, and from rigid alignment to free-form deformation, and their common solving strategies. We will report a few computation algorithms and strategies developed in our recent study and demonstrate their practical applications in computer-aided engineering, robotics, medical imaging, and forensics.

 

Short Bio:

 

Dr Xin Li is the Oskar R. Menton associate professor in School of Electrical Engineering and Computer Science at Louisiana State University (LSU). He received his B.Eng. in Computer Science from University of Science and Technology of China in 2003, and Ph.D. in Computer Science from State University of New York at Stony Brook in 2008. After graduation, he joined LSU as an assistant professor, and was promoted to an associate professor in 2014. He led the Geometric and Visual Computing lab at LSU. His research interests include geometric and visual computing, geometric data modeling and processing, and their applications in graphics, vision, robotics, forensics, and scientific computing. He has published over 80 papers in peer-reviewed journals and conference proceedings. He got IBM Faculty Award in 2011, and received LSU Flagship Faculty award in 2010, LSU C. W. Armstrong Jr. Professorship of Engineering in 2012, and Oskar R. Menton Professorship in 2013. More detail of his research can be found at http:///www.ece.lsu.edu/xinli

 

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