摘要
传统图像分割算法存在图像特征信息描述单一、分割效果差等缺点,为此,提出一种基于双特征马尔可夫随机场的图像分割方法。首先,利用像素之间的空间信息对高斯混合模型的先验概率和后验概率进行约束,建立灰度随机场。其次,在利用分数阶微分算子非线性保留图像的边缘轮廓和纹理细节的基础上,利用灰度共生矩阵描述图像的纹理特征信息,并建立纹理特征随机场。最后,设计了用于图像分割的双特征马尔可夫随机场,通过条件迭代算法优化求解标号场最大后验概率,实现图像分割。实验验证了分割算法的有效性,分割正确率达到93.9%,所提出的双特征随机场能够提高图像分割算法的鲁棒性和准确性。
Traditional image segmentation algorithms have disadvantages such as single description of image feature information and poor segmentation effect.Therefore,a dual feature Markov random field(MRF)image segmentation method is proposed.First,the spatial information between pixels is used to constrain the prior and posterior probabilities of the Gaussian mixture model(GMM)to establish a grayscale random field.Second,on the basis of non-linearly preserving the edge contours and texture details of the image by the fractional differential operator,a grayscale co-occurrence matrix is used to describe the texture feature information of the image and establish a random field of texture features.Finally,a dual feature Markov random field for image segmentation is designed,and the conditional iterative algorithm is used to optimize the maximum posterior probability of the labeled field to achieve image segmentation.Experiments verify the effectiveness of the segmentation algorithm and the segmentation accuracy is 93.9%.The proposed dual feature random field can improve the robustness and accuracy of the image segmentation algorithm.
作者
段明义
卢印举
苏玉
Duan Mingyi;Lu Yinju;Su Yu(College of Information Engineering,Zhengzhou University of Technology,Zhengzhou,Henan 450044,China;School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第22期158-166,共9页
Laser & Optoelectronics Progress
基金
河南省科技攻关计划项目(192102210120)
河南省高等学校重点科研项目(20B520036)。
关键词
图像处理
图像分割
高斯混合模型
马尔可夫随机场
迭代条件算法
image processing
image segm entation
Gaussian mixture model
Markov random field
iterative conditional algorithm
作者简介
卢印举,E-mail:luyinju2003@163.com。
