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Constrained Circular Hidden Markov Models for Recognizing Deformed Shapes

Cai, Jinhai (2006) Constrained Circular Hidden Markov Models for Recognizing Deformed Shapes. In Mohammadian, M. (Ed.) International Conference on Computational Intelligence for Modelling, Control and Automation 2006, 28 November - 1 December 2006, Sydney, NSW.

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Abstract

In this paper, we analyse the properties of the standard circular hidden Markov model (HMM) on 2D shape recognition. We point out the limitations of the circular HMMs and further propose to impose the constraint on the relationship between the initial and final states of circular HMMs to improve the performance. We develop two modified Viterbi algorithms to implement our proposal. The proposed algorithms have been tested on the database of the MPEG-7 Core Experiments Shape-1, Part B. The experiments show that both proposed algorithms can achieve better performance than that of the standard circular HMM in terms of accuracy. In particular, the second proposed algorithm, which is faster than elastic matching algorithms, has much potential due to its accuracy and speed.

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ID Code: 9340
Item Type: Conference Paper
DOI: 10.1109/CIMCA.2006.77
ISBN: 0769527310
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 04 Sep 2007
Last Modified: 29 Feb 2012 23:22

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