2-D shape classification using hidden Markov model

Author

He, Yang ; Kundu, Amlan

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York at Buffalo, Amherst, NY, USA

Volume

13

Issue

11

fYear

1991

fDate

11/1/1991 12:00:00 AM

Firstpage

1172

Lastpage

1184

Abstract

The authors present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters. This approach segments closed shapes to make classifications at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. An orientation scheme is described to make the overall classification insensitive to shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added

Keywords

Markov processes; pattern recognition; 2-D shape classification; autoregressive parameters; hidden Markov model; occlusion; orientation scheme; pattern recognition; planar shape recognition; segmentation; shape contour perturbation; shape orientation; Application software; Character recognition; Classification algorithms; Computer vision; Helium; Hidden Markov models; Pattern recognition; Predictive models; Shape; Testing;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.103276

Filename

103276