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A Comprehensive Framework for Registration and Shape Analysis of Objects

Speaker:Prof.Anuj Srivastava( Florida State University)

Time and Date: 9:30am, September. 22, 2017

Place: Room 521, Physics Building, Handan Campus

 

 

Abstract

In statistical analysis of shapes of objects, an important step is registration. The registration not only preserves important structures in the data but also leads to more parsimonious statistical models for capturing shape variability. In case of parameterized curves and surfaces, the registration step is akin to removing the parameterization variability present in mathematical representations of these objects. Taking three fundamentally different examples: (1) real-valued function data, (2) parameterized curves in Euclidean spaces, and (3) parameterized surfaces in R3, I will describe a comprehensive Riemannian framework that achieves the following goals. It provides an analysis of shapes of curves and surfaces that is invariant to standard similarity transformations and, additionally, to parameterizations of these objects. This framework, called elastic shape analysis, incorporates an optimal registration of points across objects while providing 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 medical image analysis, protein structure analysis, 3D face recognition, and human activity recognition in videos.

 

 

 

 

Biography

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 Signal Processing, IEEE Trans on Pattern Analysis and Machine Intelligence, and IEEE Trans on Image Processing. He is a fellow of the International Association of Pattern Recognition (IAPR), Institute for Electrical and Electronic Engineers (IEEE), and American Statistical Association. 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. He is an author of the textbook "Functional and Shape Data Analysis" published by Springer Statistics in 2016.

 

 

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