基于非下采样Brushlet和马尔可夫随机场的图像分割  

Image Segmentation Based on Nonsubsampled Brushlet and Markov Random Field

作　　者：刘雪娜[1] 侯宝明[1] 崔红霞[1] 

机构地区：[1]渤海大学信息科学与技术学院,辽宁锦州121013

出　　处：《计算机与现代化》2014年第2期81-85,共5页Computer and Modernization

基　　金：辽宁省教育厅一般研究项目(L2013422)

摘　　要：针对传统小波域马尔可夫随机场图像分割算法的纹理图像分割能力的不足,提出一种将非下采样Brushlet变换和马尔可夫随机场相结合的纹理图像分割方法。用非下采样Brushlet变换作为图像分割的特征场,有效地提取纹理图像中的高维奇异信息;利用高斯马尔可夫模型提取特征场的参数,考察图像中的光谱信息以及像素点的空间相关性对分割结果的影响。实验表明,本文算法可以有效地实现纹理图像分割,在检测纹理方向信息和区域一致性上较传统算法有较大的提高。In view of the shortages of conventional texture image segmentation based on Markov random field （MRF） in the wave- let domain, a segmentation method is proposed by combining nonsubsampled Brushlet transform and MRF. Nonsubsampled Brushlet transform is looked on as the feature field of the original image, which makes sure that the high dimensional singularity information of texture image is extracted effectively. And Gauss Markov model is used to compute the arguments of the feature field, which makes sure that the influences of the spectral information and the spatial correlations between pixels on the segmenta- tion result are considered. Experiments show that this algorithm can effectively achieve the texture image segmentation and it is of more great improvement than traditional algorithm in the detection of texture direction information and regional consistency.

关 键 词：非下采样Brushlet变换 马尔可夫随机场 图像分割 迭代条件模式 最大后验概率准则 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]