| 基于FGM-MRF模型的图像分割 |
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| 引用本文： | 苗晓锋,高荣国.基于FGM-MRF模型的图像分割[J].微电子学与计算机,2011,28(6):92-94,99. |
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| 作者姓名： | 苗晓锋 高荣国 |
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| 作者单位： | 1. 陕西广播电视大学,理工教学部,陕西,西安,710068
2. 西安微电子技术研究所,陕西,西安,710054 |
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| 摘 要： | 利用Ward聚类将图像进行初始分割,其结果作为基于空间邻域信息马尔可夫随机场(MRF)模型对图像再次分割的初值,图像分割的先验概率采用Ising模型,通过有限高斯混合模型(FGM)描述图像像素灰度的条件概率分布,利用期望-最大(EM)算法估计条件概率分布模型参数,用迭代条件模式(ICM)局部优化方法,获得最大后验概率(MAP)准则下的图像分割结果.通过与其他相关算法分割结果相比较,这种算法能够明显改善分割效果.
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| 关 键 词： | 图像分割 马尔可夫随机场模型 有限高斯混合模型 期望-最大算法 |
Image Segmentation Based on FGM-MRF |
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| MIAO Xiao-feng,GAO Rong-guo.Image Segmentation Based on FGM-MRF[J].Microelectronics & Computer,2011,28(6):92-94,99. |
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| Authors: | MIAO Xiao-feng GAO Rong-guo |
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| Affiliation: | 1 Polytechnic Department,Shaanxi Radio and TV University,Xi′an 710068,China;2 Xi′an Microelectronics Technology Institute,Xi′an 710054,China) |
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| Abstract: | Image segmentation is executed firstly by using Ward clustering algorithm,the result of which is served as initial value of the Markov random field(MRF) model by applying the spatial neighborhood information to complete image classification.The priori probability of the classification of the image pixel is built on Ising model,the finite Gaussian mixture(FGM) model is used to describe the conditional probability distribution of the image intensity.The expectation-maximization(EM) algorithm is applied in est... |
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| Keywords: | image segmentation Markov random field(MRF) model finite Gaussian mixture(FGM) model expectation-maximization(EM) algorithm |
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