代码搜索结果
找到约 10,000 项符合
B 的代码
b51.m
A1=imread('rice.tif');
A2=imread('testpat1.tif')
[B1,map]=gray2ind(A1,256);
[B2,map]=gray2ind(A2,256); %将两幅灰度图像转换为两幅索引图像
A=cat(3,B1,B2,B1,B2,B1,B2,B1,B2); %实现矩阵的合并
s=size(A);
for i=1:s(1
b55.m
BW=imread('circles.tif');
imshow(BW)
b6.m
%首先，创建包含了期望的频率响应的矩阵Hd
[f1,f2] = freqspace(21,'meshgrid');
Hd = zeros(21);
Hd(7:15,7:15) = 1;
Axis([-1 1 –1 1 0 1.2]),
colormap(jet(64))
mesh(f1,f2,Hd)
%期望的滤波器的频率响应见图7-16
%然后，设计通过此响应的滤波器
h
b23.m
%以saturn.tif为例，加入椒盐噪声，并在MATLAB中调用B = filter2(h,A)实现均值过滤器
I=imread('eight.tif');
J=imnoise(I,'salt &pepper',0.02);
imshow(I)
figure,imshow(J)
K1=filter2(fspecial('average',3),J)/255;
K2=filter2(f
b14.m
%对前面得到的cameraman.tif的二值化图像进行膨胀操作
I=imread('cameraman.tif');
figure,imshow(I)
J=im2bw(I);
figure,imshow(J)
SE=ones(6,2);
BW1=dilate(J,SE);
figure,imshow(BW1)
b24.m
I=imread(saturn.tif);
figure,imshow(I)
[m,n]=size(I);
J(m,n)=0;
for i=1:N
X=imnoise(I,'gaussian');
figure,imshow(X)
Y=double(X);
J=J+Y/N;
end
figure,imshow(mat2gray(J))
b79.m
t=0:pi/20:2*pi;
plot(t,sin(t));
delete(gca);
b94.m
theta=[1.2 1.1 3.4 2.1 1.5 1 6 2.6 2.4 1 8];
x=2.5:2.3:3.2;
rose(theta,x);
hline=findobj(gca,'type','line'); %获取当前坐标系中的线条对象的句柄
set(hline,'linewidth',2.0); % 将线条对象的线宽属性值设为2.0