自适应先验马尔可夫随机场模型的图像分割算法

刘光辉 1; 任庆昌 1; 孟月波 1; 2; 徐胜军 1; 2

doi:10.7652/xjtuxb201310011

您当前的位置：

首页 >

文章列表页 >

自适应先验马尔可夫随机场模型的图像分割算法

更新时间：2025-07-09

- 自适应先验马尔可夫随机场模型的图像分割算法
- An Image Segmentation Algorithm of Adaptive Prior MRF Models
- 西安交通大学学报 2013年47卷第10期页码：62-67
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院,西安,710055
- 作者简介：
- 基金信息：
  
  : 2013-04-09。作者简介: 刘光辉(1976—),男,博士生;任庆昌(通信作者),男,教授,博士生导师。基金项目: 陕西省自然科学基金资助项目(2012JM8026,2013JM8030);陕西省教育厅专项基金资助项目(2013JK1091)
- DOI：10.7652/xjtuxb201310011
  中图分类号： TP393
- 网络首发：2013-10-10，
  
  纸质出版：2013
- 稿件说明：
移动端阅览
刘光辉 1, 任庆昌 1, 孟月波 1, 等. 自适应先验马尔可夫随机场模型的图像分割算法[J]. 西安交通大学学报, 2013,47(10):62-67.

An Image Segmentation Algorithm of Adaptive Prior MRF Models[J]. 2013, 47(10): 62-67.
刘光辉 1, 任庆昌 1, 孟月波 1, 等. 自适应先验马尔可夫随机场模型的图像分割算法[J]. 西安交通大学学报, 2013,47(10):62-67. DOI： 10.7652/xjtuxb201310011.

An Image Segmentation Algorithm of Adaptive Prior MRF Models[J]. 2013, 47(10): 62-67. DOI： 10.7652/xjtuxb201310011.

摘要

针对全局同态先验马尔可夫随机场(Markov random field

MRF)模型在图像分割中不能有效利用图像局部统计特征的问题

提出了一种基于局部自适应先验MRF模型的图像分割算法。该算法基于贝叶斯理论

利用局部先验Potts模型描述图像的局部特征

建立了一种局部自适应先验MRF模型; 提出了基于区域的置信度传播(Belief Propagation

BP)算法

把图像的局部区域特征传递到全局

最终基于最大后验准则(MAP)得到图像的分割结果。实验结果表明:所提模型对于图像中的噪声或者纹理特征等具有较好的分割

分割结果明显优于全局同态先验MRF模型; 提出的自适应先验MRF模型对于图像的噪声或者纹理突变信号的干扰具有较强的鲁棒性; 算法具有较少的迭代次数和较好的分割结果

且分割时间较短。

Abstract

A segmentation algorithm based on a local adaptive prior Markov random field(MRF)model is proposed to solve the problem that the global homogeneous prior MRF model is inefficient to utilize the local statistic feature of nature images for image segmentation. The algorithm is based on Bayesian theory

utilizes local prior Potts model to represent image local features

and builds a local adaptive prior MRF model. A modified local region belief propagation(BP)algorithm is proposed over the MRF model

hence

local region features of an image are spreaded in global. The segment results of an image are obtained using a maximum posteriori(MAP)criterion. Experiment results show that the proposed adaptive prior MRF model generates more accurate segment results on the noise and texture of images than the global homogeneous prior MRF model does

and the proposed model has strong robustness on the interference of image noise or texture. The segmentation algorithm generates more accurate segmentation with less iterations and a short time.

关键词

Keywords

references

GU Dongbing, SUN Junxi. EM image segmentation algorithm based on an inhomogeneous hidden MRF model [J]. Vision Image and Signal Processing, 2005, 152(2): 184-190.

ALEXANDRE L M L, NELSON D A M, ALBERTO T, et al. Spatially non-homogeneous Potts model parameter estimation on higher-order neighborhood systems by maximum pseudo-likelihood [C]∥Proceedings of the 23rd Annual ACM Symposium on Applied Computing. New York, USA: ACM, 2008: 1733-1737.

ZHANG Xiaoling, LAM Kin-man, SHEN Lansun. Image magnification based on a blockwise adaptive Markov random field model [J]. Image and Vision Computing, 2008, 26(9): 1277-1284.

朱峰, 罗立民, 宋余庆, 等. 基于自适应空间邻域信息高斯混合模型的图像分割 [J]. 计算机研究与发展, 2011, 4(11): 2000-2007.

ZHU Feng, LUO Liming, SONG Yuqing, et al. Adaptive spatially neighborhood information Gaussian mixture model for image segmentation [J]. Journal of Computer Research and Development, 2011, 4(11): 2000-2007.

BESAG J. On the statistical analysis of dirty picture [J]. Journal of the Royal Statistical Society, 1986, 48(3): 259-302.

GEMAN S, GEMAN D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, PAMI-6(6): 721-741.

MARROQUIN J L. Probabilistic solution of inverse problems [D]. Cambridge, MA, USA: Massachusetts Institute of Technology, 1985.

JOSHI M V, CHAUDHURI S, PANUGANTI R. A learning-based method for image super-resolution from zoomed observation [J]. IEEE Transactions on System, Man, and Cybernetics, 2005, 35(3): 527-537.

PENG Bo, ZHANG Lei, ZHANG David, et al. Image segmentation by iterated region merging with localized graph cuts [J]. Pattern Recognition, 2011, 44(10/11): 2527-2538.

XU Lei, JORDAN M I. On convergence properties of the EM algorithm for Gaussian mixtures [J]. Neural Computation, 1996, 8(1): 129-151.

FELZENSZWALB P, HUTTENLOCHER D. Efficient belief propagation for early vision [J]. International Journal of Computer Vision, 2006, 70(1): 41-54.

SUN Jian, ZHENG Nanning, SHUM Heung-yeung. Stereo matching using belief propagation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 787-800.

YANG Qingxiong, WANG Liang. Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(3): 1-12.

FREEMAN W T, PASZTOR E C, CARMICHAEL O T. Learning low-level vision [J]. International Journal of Computer Vision, 2000, 40(1): 25-47.

KOMODAKIS N, TZIRITAS G. Image completion using efficient belief propagation via priority scheduling and dynamic pruning [J]. IEEE Transactions on Image Processing, 2007, 16(11): 2649-2661.

徐胜军, 韩九强, 赵亮, 等. 用于图像分割的局部区域能量最小化算法 [J]. 西安交通大学学报, 2011, 45(8): 7-12.

XU Shengjun, HAN Jiuqiang, ZHAO Liang, et al. Algorithm of minimizing local region energy for image segmentation [J]. Journal of Xi'an Jiaotong University, 2011, 45(8): 7-12.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

用于图像分割的局部区域能量最小化算法

融合边缘特征的马尔可夫随机场模型及分割算法

基于直方图偏差约束的快速模糊C均值图像分割法

递推人工蜂群的模糊划分熵多阈值分割算法

一种多源遥感图像分割的融合新策略