JSM 2005 - Toronto

Abstract #302431

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 299
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #302431
Title: Algorithms for Nonparametric Inference on Shape Manifolds
Author(s): Victor Patrangenaru*+ and Ananda Bandulasiri
Companies: Texas Tech University and Texas Tech University
Address: Department of Mathematics and Statistics, Lubbock, TX, 79409-1042,
Keywords: image analysis ; pattern recognition ; studentized extrinsic means ; shape analysis ; nonparametric bootstrap ; eigenanalysis
Abstract:

Data analysis on shape manifolds is a study of finite configurations of points modulo certain groups of transformations related to the way the images are recorded in machine vision. For example, if two planar images of one scene are obtained using a pinhole camera, the corresponding transformation is the composition of two central projections, which is a projective transformation. If the two central projections can be approximated by parallel projections, which is the case of remote views of the same planar scene, the projective transformation can be approximated by an affine transformation. Moreover, if these parallel projections are orthogonal projections on the plane of the camera, this affine transformation can be further approximated by a similarity transformation. Planar shape manifolds admit Veronese-Whitney (VW) representations in spaces of symmetric matrices, which allows defining VW-means and VW-covariance matrices of populations of planar shapes. In this talk, we will present computational algorithms for recently designed nonparametric statistics used in inference and testing for VW-means on shape manifolds that arise in high-level image analysis.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005