哈尔滨工业大学学报（英文版）2005，Vol.12Issue(1)：86-90,5.
Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule
Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule
SUN Jun-xi 1ZHANG Su 1ZHAO Yong-ming 1CHEN Ya-zhu1
作者信息
- 1. Institute of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
- 折叠
摘要
Abstract
According to the sequential maximum a posteriori probability (SMAP) rule, this paper proposes a novel multi-scale Bayesian texture segmentation algorithm based on the wavelet domain Hidden Markov Tree (HMT) model. In the proposed scheme, interscale label transition probability is directly defined and resoled by an EM algorithm. In order to smooth out the variations in the homogeneous regions, intrascale context information is considered. A Gaussian mixture model (GMM) in the redundant wavelet domain is also exploited to formulate the pixel-level statistical features of texture pattern so as to avoid the influence of the variance of pixel brightness. The performance of the proposed method is compared with the state-of-the-art HMTSeg method and evaluated by the experiment results.关键词
wavelet transform/hidden markov tree/EM algorithmKey words
wavelet transform/hidden markov tree/EM algorithm分类
信息技术与安全科学引用本文复制引用
SUN Jun-xi,ZHANG Su,ZHAO Yong-ming,CHEN Ya-zhu..Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule[J].哈尔滨工业大学学报（英文版）,2005,12(1):86-90,5.
