Contour analysis using time-varying autoregressive model

Author

Eom, Kie B.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA

Volume

2

fYear

2000

fDate

10-13 Sept. 2000

Firstpage

891

Abstract

Contour modeling by a time-varying autoregressive (TVAR) model is considered. A least squares estimator of the TVAR model parameters is presented, and the maximum likelihood approach for determining the model order is also presented. In the experiment, curvature extrema points of synthesized contours are detected from the time frequency distribution estimated with TVAR model. In the classification experiment with contours of various planar shapes, about 97% of samples are correctly classified.

Keywords

autoregressive processes; image classification; least squares approximations; maximum likelihood estimation; time-frequency analysis; TVAR model parameters; classification experiment; contour analysis; curvature extrema points; least squares estimator; maximum likelihood estimation; model order; planar shapes; synthesized contours; time frequency distribution; time-varying AR model; time-varying autoregressive model; Covariance matrix; Frequency estimation; Gaussian processes; Maximum likelihood detection; Maximum likelihood estimation; Parameter estimation; Polynomials; Shape; Testing; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC, Canada

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.899857

Filename

899857