A Computational Framework for Statistical Shape Analysis
Visiting Researcher Professor Anuj Srivastava (Department of Statistics Florida State University)
NICTA SEMINAR Computer VisionDATE: 2013-09-19
TIME: 16:00:00 - 17:00:00
LOCATION: NICTA - 7 London Circuit
CONTACT: JavaScript must be enabled to display this email address.
ABSTRACT:
Dial in number: 61262112668.
Shape analysis and modeling of 2D and 3D objects has important applications in many branches of science and engineering. The general goals in shape analysis include: derivation of efficient shape metrics, computation of shape templates, representation of dominant shape variability in a shape class, and development of probability models that characterize shape variation within and across classes. While past work on shape analysis is dominated by point representations -- finite sets of ordered or triangulated points on objects' boundaries -- the emphasis has lately shifted to continuous formulations. The shape analysis of parameterized curves and surfaces introduces an additional shape invariance, the re-parametrization group, in additional to the standard invariants of rigid motions and global scales. Treating re-parameterization as a tool for registration of points across objects, we incorporate this group in shape analysis, in the same way orientation is handled in Procrustes analysis. For shape analysis of parameterized curves, I will describe elastic Riemannian metrics and corresponding mathematical representations, called square-root functions, that allows optimal registration and analysis using simple tools. This framework provides proper metrics, geodesics, and sample statistics of shapes. These sample statistics are further useful in statistical modeling of shapes in different shape classes. I will demonstrate these ideas using applications from computer vision, medical image analysis, protein structure analysis, 3D face recognition, and human activity recognition in videos.
BIO:
Anuj Srivastava is a Professor in the Department of Statistics at the Florida State University in Tallahassee, FL. He obtained his PhD degree in Electrical Engineering from Washington University in St. Louis in 1996 and was a postdoc at Division of Applied Mathematics at Brown University during 1996-1997. He joined the Department of Statistics at the Florida State University in 1997 as an Assistant Professor. Subsequently, he was promoted to Associate Professor in 2003 and to full Professor in 2007. He has held visiting positions at INRIA, Sophia Antipolis, France; Universit A Catholique de Louvain, Belgium; and University of Lille, France. He is also a recipient of the Durham International Senior Fellowship from Durham University in UK for 2014. His areas of research include statistics on nonlinear manifolds, statistical image papers in refereed journals and proceedings of refereed international conferences. He has been an associate editor for Journal of Statistical Planning and Inference, IEEE Transactions on Signal Processing, and IEEE Transactions on Pattern Analysis and Machine Intelligence.
