标签：
| 灰度共生矩阵提取纹理特征源码 %************************************************************************** % 图像检索——纹理特征 %基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵 %所用图像灰度级均为256 %参考《基于颜色空间和纹理特征的图像检索》 %function : T=Texture(Image) %Image : 输入图像数据 %T : 返回八维纹理特征行向量 %************************************************************************** function T = vtex(Gray) Gray = imread(‘压痕.bmp‘); M=size(Gray,1); N=size(Gray,2); %M = 256; %N = 256; %-------------------------------------------------------------------------- %1.将各颜色分量转化为灰度 %-------------------------------------------------------------------------- %Gray = double(0.3*Image(:,:,1)+0.59*Image(:,:,2)+0.11*Image(:,:,3)); %-------------------------------------------------------------------------- %2.为了减少计算量，对原始图像灰度级压缩，将Gray量化成16级 %-------------------------------------------------------------------------- for i = 1:M for j = 1:N for n = 1:256/16 if (n-1)*16<=Gray(i,j)&&Gray(i,j)<=(n-1)*16+15 Gray(i,j) = n-1; end end end end %-------------------------------------------------------------------------- %3.计算四个共生矩阵P,取距离为1，角度分别为0,45,90,135 %-------------------------------------------------------------------------- P = zeros(16,16,4); for m = 1:16 for n = 1:16 for i = 1:M for j = 1:N if Gray(i,j)==m-1 if j<N&&Gray(i,j+1)==n-1 P(m,n,1) = P(m,n,1)+1; end if i>1&&j<N&&Gray(i-1,j+1)==n-1 P(m,n,2) = P(m,n,2)+1; end if i<M&&Gray(i+1,j)==n-1 P(m,n,3) = P(m,n,3)+1; end if i<M&&j<N&&Gray(i+1,j+1)==n-1 P(m,n,4) = P(m,n,4)+1; end end end end P(n,m,1) = P(m,n,1); P(n,m,2) = P(m,n,2); P(n,m,3) = P(m,n,3); P(n,m,4) = P(m,n,4); if m==n P(m,n,:) = P(m,n,:)*2; end end end %%--------------------------------------------------------- % 对共生矩阵归一化 %%--------------------------------------------------------- for n = 1:4 P(:,:,n) = P(:,:,n)/sum(sum(P(:,:,n))); end %-------------------------------------------------------------------------- %4.对共生矩阵计算能量、熵、惯性矩、相关4个纹理参数 %-------------------------------------------------------------------------- H = zeros(1,4); I = H; Ux = H; Uy = H; deltaX= H; deltaY = H; C =H; for n = 1:4 E(n) = sum(sum(P(:,:,n).^2)); %%能量 for i = 1:16 for j = 1:16 if P(i,j,n)~=0 H(n) = -P(i,j,n)*log(P(i,j,n))+H(n); %%熵 end I(n) = (i-j)^2*P(i,j,n)+I(n); %%惯性矩 Ux(n) = i*P(i,j,n)+Ux(n); %相关性中μx Uy(n) = j*P(i,j,n)+Uy(n); %相关性中μy end end end for n = 1:4 for i = 1:16 for j = 1:16 deltaX(n) = (i-Ux(n))^2*P(i,j,n)+deltaX(n); %相关性中σx deltaY(n) = (j-Uy(n))^2*P(i,j,n)+deltaY(n); %相关性中σy C(n) = i*j*P(i,j,n)+C(n); end end C(n) = (C(n)-Ux(n)*Uy(n))/deltaX(n)/deltaY(n); %相关性 end %-------------------------------------------------------------------------- %求能量、熵、惯性矩、相关的均值和标准差作为最终8维纹理特征 %-------------------------------------------------------------------------- T(1) = mean(E); T(2) = sqrt(cov(E)); T(3) = mean(H); T(4) = sqrt(cov(H)); T(5) = mean(I); T(6) = sqrt(cov(I)); T(7) = mean(C); T(8) = sqrt(cov(C)); |
标签：
原文地址：http://www.cnblogs.com/qxzy/p/4344524.html
