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2010 International Conference on Digital Image Computing: Techniques and Applications
Geometric Invariant Shape Classification Using Hidden Markov Model
Sydney, New South Wales Australia
December 01-December 03
ISBN: 978-0-7695-4271-3
In this paper we propose a novel approach for geometric shape classification by using shape simplification and discrete Hidden Markov Model (HMM). The HMM is constructed using the landmark points obtained from the shape simplification for each shape image in the dataset. Some useful strategies have been employed for the constructed HMM for geometric shape classification. Experimental results based on the common MPEG7 CE shapes database shows that our proposed method can achieve very good accuracy in different kinds of shapes.
Index Terms:
Shape classification, simplification, Hidden Markov Model geometric
Citation:
Chi-Man Pun, Cong Lin, "Geometric Invariant Shape Classification Using Hidden Markov Model," dicta, pp.406-410, 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010
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