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Proceedings Paper

Shape classification using Hidden Markov Model and structural feature
Author(s): Bangwang Xie; Zhiyong Wang; Jiajun Wang
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Paper Abstract

A novel shape classification method based on Hidden Markov Models (HMMs) is proposed in the paper. Instead of characterizing points along an object contour, our method employs HMMs to model the relationship among structural segments of the contour. Firstly, an object contour is partitioned into segments at points with zero curvature value. Secondly, each segment is represented with structural features. Finally, a HMMs is utilized to characterize the object contour by treating each segment as an observation of a hidden state. Promising experimental results obtained on two popular shape datasets demonstrate that the proposed method is efficient in classifying shapes, particularly unclosed shapes and similar shapes.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880C (15 November 2007); doi: 10.1117/12.743168
Show Author Affiliations
Bangwang Xie, Soochow Univ. (China)
Zhiyong Wang, Univ. of Sydney (Australia)
Jiajun Wang, Soochow Univ. (China)
Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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