An Image Segmentation Algorithm of Adaptive Prior MRF Models

doi:10.7652/xjtuxb201310011

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An Image Segmentation Algorithm of Adaptive Prior MRF Models

更新时间：2025-07-09

- An Image Segmentation Algorithm of Adaptive Prior MRF Models
- Vol. 47, Issue 10, Pages: 62-67(2013)
- 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：
- DOI：10.7652/xjtuxb201310011
  CLC： TP393
- Online First：10 October 2013，
  
  Published：2013
- 稿件说明：
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An Image Segmentation Algorithm of Adaptive Prior MRF Models[J]. 2013, 47(10): 62-67.
DOI：

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.

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references

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