引用本文: 郑晨, 王雷光, 胡亦钧, 秦前清. 利用小波域多尺度模糊MRF模型进行纹理分割[J]. 武汉大学学报 ( 信息科学版), 2010, 35(9): 1074-1078.
ZHENG Chen, WANG Leiguang, HU Yijun, QIN Qianqing. Texture Segmentation Based on Multiscale Fuzzy Markov Random Field Model in Wavelet Domain[J]. Geomatics and Information Science of Wuhan University, 2010, 35(9): 1074-1078.
 Citation: ZHENG Chen, WANG Leiguang, HU Yijun, QIN Qianqing. Texture Segmentation Based on Multiscale Fuzzy Markov Random Field Model in Wavelet Domain[J]. Geomatics and Information Science of Wuhan University, 2010, 35(9): 1074-1078.

## Texture Segmentation Based on Multiscale Fuzzy Markov Random Field Model in Wavelet Domain

• 摘要: 针对小波域多尺度马尔科夫随机场模型(Markov random field,MRF)对信息利用不充分的特点,在模型中引入模糊理论,提出了一种新的小波域多尺度MRF模型。新模型定义了相应的模糊概率场,通过模糊概率场描述每个小波域各尺度上像素的类别隶属度;根据模糊概率场估计了对应的特征场模型参数,参数的估计考虑了同尺度所有位置的特征信息;根据特征场模型导出了对应的示性场模型,用其反映每个像素的类别能量。利用贝叶斯准则给出了3步交互迭代算法,获得了分割结果。

Abstract: Since the inadequate use of statistical information in the multiscale Markov random field model in the wavelet domain, the fuzzy theory is introduced into the model and a new multiscale fuzzy Markov random field model in the wavelet domain is presented. Firstly, the model which is based on the fuzzy theory defines a fuzzy probability field, which is used to describe the degree of every pixel belong to which segmentation region in each scale in wavelet domain. During evaluating parameters stage of the following feature field, the whole pixels' features in the same scale are taken into account. Then, the indicator field which reflects the energy of every pixel that belong to certain segmentation region is induced from the feature field. Finally, a three-step interaction iterative segmentation steps based on the Bayes rule is induced. The experiments compared with the ICM and MRMRF algorithms have proved the proposed method’s availability

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