Abstract #301596

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); 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.

Back to main JSM 2003 Program page
JSM 2003 Abstract #301596
Activity Number: 319
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #301596
Title: Tracking Edges and Vertices in an Image
Author(s): Christian Rau*+ and Peihua Qiu and Peter Hall
Companies: Australian National University and University of Minnesota and Australian National University
Address: Ctr. for Mathematics and Its Applications, Canberra, , 0200, Australia
Keywords: boundary estimation ; edge detection ; local least squares ; local likelihood ; nonparametric curve estimation ; image processing
Abstract:

In a range of imaging problems, particularly those where the images are of man-made objects, edges join at points which comprise three or more distinct boundaries between textures. In such cases, the set of edges in the plane forms a mathematical graph. Smooth edges in the graph meet one another at junctions, called 'vertices,' the 'degrees' of which denote the respective numbers of edges that join there. Conventional image reconstruction methods do not always draw clear distinctions among different degrees of a vertex. In this paper we suggest an alternative approach to edge reconstruction, which combines a vertex classification step with an edge-trackin routine. While it is still local, and hence adaptive, in character, the specific focus on vertex degree estimation alleviates the previously mentioned problem with conventional estimators. The technique is based on local least squares, or local likelihood in the case of Gaussian data. Theoretical properties are discussed, and a finite-sample study explores its numerical properties.

  • 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 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003