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Bayesian document segmentation based on complex wavelet domain hidden Markov tree models

Sun, J and Gu, D and Cai, H and Liu, G and Chen, G (2008) Bayesian document segmentation based on complex wavelet domain hidden Markov tree models. In: UNSPECIFIED, ? - ?.

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Abstract

A texture-based Bayesian document segmentation method is investigated in this paper. This Bayesian method is used to fuse texture likelihood and prior contextual knowledge to achieve document segmentation. The texture likelihood is based on a complex wavelet domain hidden Markov tree (HMT) model and the prior contextual is based on a hybrid tree model. A redundant wavelet domain Gaussian mixture model is employed to capture pixel-level features in the HMT model. Several document images are segmented to verify the proposed method. Comparisons with other corresponding models are made. © 2008 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Proceedings of the 2008 IEEE International Conference on Information and Automation, ICIA 2008
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 14 Jan 2013 13:09
Last Modified: 23 Jan 2019 00:16
URI: http://repository.essex.ac.uk/id/eprint/4854

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