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⛄ 内容介绍
本文提出了一种新的基于区域的变分水平集活动轮廓模型图像分割公式。在我们的模型中，局部图像强度为用不同均值和方差的高斯分布描述。我们定义了一个具有水平集函数和局部平均值的局部高斯分布拟合能量方差作为变量。能量最小化通过交错电平集实现迭代过程中局部强度均值和方差的演化和估计。局部强度的均值和方差被认为是空间变化的处理空间强度变化的强度不均匀性和噪声的函数（例如乘性噪声）。此外，我们的模型能够区分具有以下特征的区域相似的强度意味着不同的方差。这可以通过应用我们的基于不同区域纹理模式的含噪纹理图像处理方法可以从局部强度方差中区分出来。对比实验显示该方法的优点。
⛄ 部分代码
% This is a demo for segmentation using local gaussian distribution (LGD)
% fitting energy
%
% Reference: <Li Wang, Lei He, Arabinda Mishra, Chunming Li.
% Active Contours Driven by Local Gaussian Distribution Fitting Energy.
% Signal Processing, 89(12), 2009,p. 2435-2447>
clc;clear all;close all;
Img=imread('3.bmp');
Img = double(Img(:,:,1));
NumIter = 1000; %iterations
timestep=0.1; %time step
mu=0.1/timestep;% level set regularization term, please refer to "Chunming Li and et al. Level Set Evolution Without Re-initialization: A New Variational Formulation, CVPR 2005"
sigma = 3;%size of kernel
epsilon = 1;
c0 = 2; % the constant value
lambda1=1.03;%outer weight, please refer to "Chunming Li and et al, Minimization of Region-Scalable Fitting Energy for Image Segmentation, IEEE Trans. Image Processing, vol. 17 (10), pp. 1940-1949, 2008"
lambda2=1.0;%inner weight
%if lambda1>lambda2; tend to inflate
%if lambda1<lambda2; tend to deflate
nu = 0.0005*255*255;%length term
alf = 20;%data term weight
figure,imagesc(uint8(Img),[0 255]),colormap(gray),axis off;axis equal
[Height Wide] = size(Img);
[xx yy] = meshgrid(1:Wide,1:Height);
phi = (sqrt(((xx - 64).^2 + (yy - 76).^2 )) - 15);
phi = sign(phi).*c0;
Ksigma=fspecial('gaussian',round(2*sigma)*2 + 1,sigma); % kernel
ONE=ones(size(Img));
KONE = imfilter(ONE,Ksigma,'replicate');
KI = imfilter(Img,Ksigma,'replicate');
KI2 = imfilter(Img.^2,Ksigma,'replicate');
figure,imagesc(uint8(Img),[0 255]),colormap(gray),axis off;axis equal,
hold on,[c,h] = contour(phi,[0 0],'r','linewidth',1); hold off
pause(0.5)
tic
for iter = 1:NumIter
phi =evolution_LGD(Img,phi,epsilon,Ksigma,KONE,KI,KI2,mu,nu,lambda1,lambda2,timestep,alf);
if(mod(iter,50) == 0)
figure(2),
imagesc(uint8(Img),[0 255]),colormap(gray),axis off;axis equal,title(num2str(iter))
hold on,[c,h] = contour(phi,[0 0],'r','linewidth',1); hold off
pause(0.02);
end
end
toc
⛄ 运行结果
