1简介
In this project1, we fifirst study the Gaussian-based hidden Markov random fifield (HMRF) model and its expectation maximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random fifield. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.








2 部分代码
%% This script generates an input 3D image% Copyright by Quan Wang, 2012/12/16% Please cite: Quan Wang. GMM-Based Hidden Markov Random Field for% Color Image and 3D Volume Segmentation. arXiv:1212.4527 [cs.CV], 2012.clear;clc;close all;I=zeros([50 50 50]);center=[25 25 25];R=20;for i=1:50for j=1:50for k=1:50d=norm([i j k]-center);if d<RI(i,j,k)=100;endendendendI=I+rand([50 50 50])*120;I=round(I);delete Image.raw;fid=fopen('Image.raw','w');fwrite(fid,I,'uint8');fclose(fid);
3 仿真结果

4 参考文献
[1] Wang Q . GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation[J]. Computer Science, 2012.
博主简介:擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真,相关matlab代码问题可私信交流。
部分理论引用网络文献,若有侵权联系博主删除。
本文研究了基于高斯的隐马尔可夫随机场(HMRF)模型及其期望最大化(EM)算法,并将其推广到高斯混合模型。该算法在MATLAB中实现,并应用于彩色图像和3D体积的分割问题。通过生成3D输入图像并进行实验,展示了算法的有效性。参考文献中提供了详细信息和代码示例。
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