1 简介

以图像分割为研究内容,分析了图像的空频域特征,并在此基础之上提出了一种基于模糊局部信息C均值聚类的分割算法.由于FLICM算法充分利用局部空间信息和局部灰度信息,能够很好地描述模糊性,对于边界不清晰的太赫兹图像有很好地效果.采用不同成像条件下的多类太赫兹图像进行实验,结果表明此方法能够很好地克服边缘模糊,随机噪声,条纹噪声等干扰,分割所得目标轮廓完整,准确,有较高的精度,为进一步的图像目标识别打下了良好的基础.

2 部分代码

clear; clc; data=randn(310,2); [center,U,obj_fcn] = fcm(data,2); plot(data(:,1), data(:,2),'o'); hold on; maxU = max(U); % Find the data points with highest grade of membership in cluster 1 index1 = find(U(1,:) == maxU); % Find the data points with highest grade of membership in cluster 2 index2 = find(U(2,:) == maxU); %line(data(index1,1),data(index1,2),'marker','*','color','g'); %line(data(index2,1),data(index2,2),'marker','*','color','r'); % Plot the cluster centers plot([center([1 2],1)],[center([1 2],2)],'*','color','k'); hold off; % I = imread('lena.jpg'); %I = imread('eight.tif'); % I = imread('romantic.jpg'); I = imread('bacteria.jpg'); I=rgb2gray(I); figure,imshow(I,[]); title('原图'); g=imnoise(I,'salt & pepper',0.02); % 给图像添加高斯噪声 figure,imshow(g,[]); title('添加高斯噪声后的图像'); g = double(g); [m,n] = size(g); k=2; % ---------fcm---------------- fcm_label=zeros(m*n,1); [O,U,obj_fcn1] =fcm(g(:), k); maxU = max(U); for j=1:k index = find(U(j, :) == maxU); fcm_label(index) = j; end fcm_result=reshape(fcm_label,[m n]); figure, imshow(fcm_result,[]); title('fcm分割结果'); % ------------------------ FLICM法------------------------ r = 3; % 求G时，邻域窗的大小 [neighbor_pos,window_d]=FLICM_find_neighbor(g,r); FLICM_label=zeros(m*n,1); t=cputime; X = g(:); [O10,U10,obj_fcn10] = FLICM(X,k,neighbor_pos,window_d); time_FLICM=cputime-t; maxU10 = max(U10); for j=1:k index = find(U10(j, :) == maxU10); FLICM_label(index) = j; end labels10=reshape(FLICM_label,[m n]); figure, imshow(labels10,[]); title('flicm分割结果');

3 仿真结果

4 参考文献

[1]雷萌, 黄志坚, and 马芳粼. "一种基于局部信息模糊聚类的太赫兹图像分割算法." 制造业自动化 37.12(2015):3.

部分理论引用网络文献，若有侵权联系博主删除。