On Advances in the Role of Differential Geometry in Computer Vision and Pattern Recognition
Dr. Anuj Srivastava of Florida State University
Tuesday, November 29, 2016 · 11:00AM · HEC 101
The problem area of computer vision and pattern recognition (CVPR) involves modeling low-dimensional structures of interest, using high-dimensional data. It is thus natural to use geometry and statistics as basic tools for analysis in CVPR. Geometry is the language for studying structures while statistical is the science of modeling variability. The recent decade have seen substantial advances in use of geometry in vision algorithms. Examples include shape analysis, action recognition, covariance tracking, medical image analysis, and so on. In this talk, I will introduce some basic elements of differential geometry and describe some efficient vision algorithms that rely on these ideas.
Anuj Srivastava is a Professor in the Department of Statistics and a Distinguished Research Professor at the Florida State University. His areas of research include statistical analysis on nonlinear manifolds, statistical computer vision, functional data analysis, and statistical shape theory. He has been the associate editor for the Journal of Statistical Planning and Inference, IEEE Trans. on SP, IEEE Trans. on PAMI, and IEEE Trans. on IP. Currently he is an associated editor of CVIU. He is a fellow of the International Association of Pattern Recognition (IAPR) and a fellow of the Institute for Electrical and Electronic Engineers (IEEE). He has held several visiting positions at European universities, including INRIA, France; the University of Lille, France as a Fulbright Scholar; and Durham University, UK as a Senior Fellow.