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Proceedings Paper

Texture image segmentation using Brushlet-domain hidden Markov models
Author(s): Fang Liu; Kai Yang; Hongxia Hao; Biao Hou; Hua Zhong
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Paper Abstract

By researching the Brushlet domain coefficients of texture images, we found that the distribution of the magnitudes of Brushlet domain coefficients roughly meet rayleigh distribution. And there are correlations between Brushlet coefficients in adjacent scales. Therefore, Rayleigh Mixture Model (RMM) is used to characterize the statistics of the magnitudes of Brushlet coefficients. To capture the inter-scale persistence of Brushlet coefficients, a "four to four" models with markov property is adopted in this paper. On the basis, by combining with the multi-scale Bayesian segmentation method, we propose a multiscale Bayesian texture segmentation algorithm that is based on a Brushlet domain hidden Markov tree (BruHMT) model. The experiment results indicate that our method is feasible and effective. Especially for coarse texture, our method is superior than texture segmentation method using Wavelet domain hidden Markov tree (WHMT) model.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749854 (30 October 2009); doi: 10.1117/12.833002
Show Author Affiliations
Fang Liu, Xidian Univ. (China)
Ministry of Education (China)
Kai Yang, Xidian Univ. (China)
Ministry of Education (China)
Hongxia Hao, Xidian Univ. (China)
Ministry of Education (China)
Biao Hou, Ministry of Education (China)
Xidian Univ. (China)
Hua Zhong, Ministry of Education (China)
Xidian Univ. (China)


Published in SPIE Proceedings Vol. 7498:
MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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