matlab 图像边缘检测
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I=imread('Mary.bmp');
imshow(I);
title('宿主图像');
BW1=edge(I,'canny');
BW2=edge(I,'log');
BW3=edge(I,'soble');
BW4=edge(I,'Roberts');
BW5=edge(I,'Prewitt');
figure;
subplot(2,3,1);imshow(BW1);
title('canny算子');
subplot(2,3,2);imshow(BW2);
title('log算子');
subplot(2,3,3);imshow(BW3);
title('soble算子');
subplot(2,3,4);imshow(BW4);
title('Roberts算子');
subplot(2,3,5);imshow(BW5);
title('Prewitt算子');
imwrite(BW1,'cannyedge.bmp');
就出现一个图像,
error
??? Error using ==> edge>parse_inputs
Invalid input string: 'soble'.
Error in ==> edge at 161
[a,method,thresh,sigma,H,kx,ky] = parse_inputs(varargin:);
图像的亚像素边缘检测 MATLAB代码
求助亚像素边缘检测的程序
本人急需亚像素边缘检测算法的程序,只要是亚像素边缘检测的都可以。帮帮忙! 我这有基于小波的, 谁能给改成基于小波的图像亚像素边缘检测,发我邮箱:410756340@qq.com
%装载并显示原始图像
load bust;
%加入含噪
init=2055615866;
randn('seed',init);
X1=X+20*randn(size(X));
subplot(221);image(X1);
%colormap(map);
title('原始图像');axis square;
%用小波db4对图像X进行一层小波包分解
T=wpdec2(X1,1,'db4');
%重构图像近似部分
A=wprcoef(T,[1 0]);
subplot(222);image(A);
title('图像的近似部分');
axis square;
%边缘检测
%%原图像的边缘检测
BW1 = edge(A,'sobel');
subplot(223);imshow(BW1);
title('原图像的边缘');
axis square;
%图像近似部分的边缘检测
BW2= edge(X1,'sobel');
subplot(224);imshow(BW2);
title('图像近似部分的边缘');
axis square;
做出来追加100分
做出来追加100分
哥您是会做 专家级的??我是看不懂 对不对 您给个准信 行吗???????谢谢了
denoising tools in the Wavelet Toolbox.
This demo uses Wavelet Toolbox functions.
% Set signal to noise ratio and set rand seed.
sqrt_snr = 3; init = 2055615866;
% Generate original signal and a noisy version adding
% a standard Gaussian white noise.
[xref,x] = wnoise(3,11,sqrt_snr,init);
% Denoise noisy signal using soft heuristic SURE thresholding
% and scaled noise option, on detail coefficients obtained
% from the decomposition of x, at level 5 by sym8 wavelet.
% Generate original signal and a noisy version adding
% a standard Gaussian white noise.
lev = 5;
xd = wden(x,'heursure','s','one',lev,'sym8');
% Denoise noisy signal using soft SURE thresholding.
xd = wden(x,'rigrsure','s','one',lev,'sym8');
% Denoise noisy signal using fixed form threshold with
% a single level estimation of noise standard deviation.
xd = wden(x,'sqtwolog','s','sln',lev,'sym8');
% Denoise noisy signal using fixed minimax threshold with
% a multiple level estimation of noise standard deviation.
xd = wden(x,'minimaxi','s','sln',lev,'sym8');
% If many trials are necessary, it is better to perform
% decomposition one time and threshold it many times :
% decomposition.
[c,l] = wavedec(x,lev,'sym8');
% threshold the decomposition structure [c,l].
xd = wden(c,l,'minimaxi','s','sln',lev,'sym8');
% Load electrical signal and select a part.
load leleccum; indx = 2600:3100;
x = leleccum(indx);
% Use wdencmp for signal de-noising.
% find default values (see ddencmp).
[thr,sorh,keepapp] = ddencmp('den','wv',x);
% denoise signal using global thresholding option.
xd = wdencmp('gbl',x,'db3',2,thr,sorh,keepapp);
% Some trial examples without commands counterpart.
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 5;
% [xref,x] = wnoise(1,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 4;
% [xref,x] = wnoise(2,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).
% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
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