susan算子matlab代码,计算机视觉：matlab实现9种边缘检测算子

9种边缘算子

Canny算子

Sobel算子

Susan算子

Prewitt算子

Laplace算子

LOG算子

Roberts算子

Kirsch算子

普通梯度方法

其中，

roberts,canny,sobel,log,prewitt在matlab中都可直接用一行代码来实现：

BW = edge(I,method,threshold)

main.m

clc; clear; close all;

Img = imread('CXK.png');

Img=rgb2gray(Img);

Img_canny = edge(Img,'canny'); %canny

Img_roberts = edge(Img,'roberts'); %reberts

Img_prewitt = edge(Img,'prewitt'); %prewitt

Img_sobel=edge(Img,'sobel'); %sobel

Img_log=edge(Img,'log'); %log

Img_lap=laplace(Img,0.05); %laplace

Img_susan=susan(Img,0.2); %susan

Img_kir=kirsch(Img); %kirsch

[Img_x,Img_y,Img_t]=grd(Img); %普通梯度法

figure();

subplot(231);imshow(Img_lap);title('laplace');

subplot(232);imshow(Img_log);title('log');

subplot(233);imshow(Img_canny);title('canny');

subplot(234);imshow(Img_prewitt);title('prewitt');

subplot(235);imshow(Img_sobel);title('sobel');

subplot(236);imshow(Img_roberts);title('roberts');

figure();

subplot(1,2,1);imshow(Img);title('原图');

subplot(1,2,2);imshow(Img_kir);title('kirsch');

figure();

subplot(1,2,1);imshow(Img);title('原图');

subplot(1,2,2);imshow(Img_susan);title('susan');

figure()

subplot(1,3,1);imshow(Img_x);title('gradient-x');

subplot(1,3,2);imshow(Img_y);title('gradient-y');

subplot(1,3,3);imshow(Img_t);title('gradient-t');

susan.m

function image_out = susan(im,threshold)

% 功能：实现运用SUNSAN算子进行边缘检测

% 输入：image_in-输入的待检测的图像

% threshold-阈值

% 输出：image_out-检测边缘出的二值图像

% 将输入的图像矩阵转换成double型

d = length(size(im));

if d==3

image=double(rgb2gray(im));

elseif d==2

image=double(im);

end

% 建立SUSAN模板

mask = ([ 0 0 1 1 1 0 0 ;0 1 1 1 1 1 0;1 1 1 1 1 1 1;1 1 1 1 1 1 1;1 1 1 1 1 1 1;0 1 1 1 1 1 0;0 0 1 1 1 0 0]);

R=zeros(size(image));

% 定义USAN 区域

nmax = 3*37/4;

[a b]=size(image);

new=zeros(a+7,b+7);

[c d]=size(new);

new(4:c-4,4:d-4)=image;

for i=4:c-4

for j=4:d-4

current_image = new(i-3:i+3,j-3:j+3);

current_masked_image = mask.*current_image;

% 调用susan_threshold函数进行阈值比较处理

current_thresholded = susan_threshold(current_masked_image,threshold);

g=sum(current_thresholded(:));

if nmax

R(i,j) = g-nmax;

else

R(i,j) = 0;

end

end

end

image_out=R(4:c-4,4:d-4);

susan_threshold.m

function thresholded = susan_threshold(image,threshold)

% 功能：设定SUSAN算法的阈值

[a b]=size(image);

intensity_center = image((a+1)/2,(b+1)/2);

temp1 = (image-intensity_center)/threshold;

temp2 = temp1.^6;

thresholded = exp(-1*temp2);

kirsch.m

function Img_kir=kirsch(Img)

%---------------------------------------------------------------

%对图象进行预处理

%对图象进行均值滤波

Img2=filter2(fspecial('average',3),Img);

%对图象进行高斯滤波

Img3=filter2(fspecial('gaussian'),Img2);

%利用小波变换对图象进行降噪处理

[thr,sorh,keepapp]=ddencmp('den','wv',Img3); %获得除噪的缺省参数

Img4=wdencmp('gbl',Img3,'sym4',2,thr,sorh,keepapp);%图象进行降噪处理

%---------------------------------------------------------------------

%提取图象边缘

t=[0.8 1.0 1.5 2.0 2.5].*10^5 ; %设定阈值

Img5=double(Img4);

[m,n]=size(Img5);

g=zeros(m,n);

d=zeros(1,8);

%利用Kirsch算子进行边缘提取

for i=2:m-1

for j=2:n-1

d(1) =(5*Img5(i-1,j-1)+5*Img5(i-1,j)+5*Img5(i-1,j+1)-3*Img5(i,j-1)-3*Img5(i,j+1)-3*Img5(i+1,j-1)-3*Img5(i+1,j)-3*Img5(i+1,j+1))^2;

d(2) =((-3)*Img5(i-1,j-1)+5*Img5(i-1,j)+5*Img5(i-1,j+1)-3*Img5(i,j-1)+5*Img5(i,j+1)-3*Img5(i+1,j-1)-3*Img5(i+1,j)-3*Img5(i+1,j+1))^2;

d(3) =((-3)*Img5(i-1,j-1)-3*Img5(i-1,j)+5*Img5(i-1,j+1)-3*Img5(i,j-1)+5*Img5(i,j+1)-3*Img5(i+1,j-1)-3*Img5(i+1,j)+5*Img5(i+1,j+1))^2;

d(4) =((-3)*Img5(i-1,j-1)-3*Img5(i-1,j)-3*Img5(i-1,j+1)-3*Img5(i,j-1)+5*Img5(i,j+1)-3*Img5(i+1,j-1)+5*Img5(i+1,j)+5*Img5(i+1,j+1))^2;

d(5) =((-3)*Img5(i-1,j-1)-3*Img5(i-1,j)-3*Img5(i-1,j+1)-3*Img5(i,j-1)-3*Img5(i,j+1)+5*Img5(i+1,j-1)+5*Img5(i+1,j)+5*Img5(i+1,j+1))^2;

d(6) =((-3)*Img5(i-1,j-1)-3*Img5(i-1,j)-3*Img5(i-1,j+1)+5*Img5(i,j-1)-3*Img5(i,j+1)+5*Img5(i+1,j-1)+5*Img5(i+1,j)-3*Img5(i+1,j+1))^2;

d(7) =(5*Img5(i-1,j-1)-3*Img5(i-1,j)-3*Img5(i-1,j+1)+5*Img5(i,j-1)-3*Img5(i,j+1)+5*Img5(i+1,j-1)-3*Img5(i+1,j)-3*Img5(i+1,j+1))^2;

d(8) =(5*Img5(i-1,j-1)+5*Img5(i-1,j)-3*Img5(i-1,j+1)+5*Img5(i,j-1)-3*Img5(i,j+1)-3*Img5(i+1,j-1)-3*Img5(i+1,j)-3*Img5(i+1,j+1))^2;

g(i,j) = max(d);

end

end

%显示边缘提取后的图象

for k=1:5

for i=1:m

for j=1:n

if g(i,j)>t(k)

Img_kir(i,j)=255;

else

Img_kir(i,j)=0;

end

end

end

end

laplace.m

function Img_lap=laplace(Img,threshold)

lenna_3=mat2gray(Img); %图像矩阵的归一化

[m,n]=size(lenna_3);

lenna_4=lenna_3; %保留图像的边缘一个像素

L=0;

t=threshold; %设定阈值

%Laplace算子

for j=2:m-1

for k=2:n-1

L=abs(4*lenna_3(j,k)-lenna_3(j-1,k)-lenna_3(j+1,k)-lenna_3(j,k+1)-lenna_3(j,k-1));

if(L > t)

Img_lap(j,k)=255; %白

else

Img_lap(j,k)=0; %黑

end

end

end

grd.m

function [Img_x,Img_y,Img_t]=grd(Img)

I=double(Img);

[x, y]=gradient(I);

t=sqrt(x.^2+y.^2);

Img_x=I;Img_y=I;Img_t=I;

Img_t(t>10)=255;

Img_t(t<10)=0;

%figure,imshow(G);title('gradient-t');

Img_x(x>2)=255;

Img_x(x<2)=0;

%figure,imshow(G);title('gradient-x');

Img_y(y>2)=255;

Img_y(y<2)=0;

%figure,imshow(G);title('gradient-y');

使用的原图如下：

CXK.png

结果如下：

laplace,log,canny,prewitt,sobel,roberts

kirsch

普通梯度法

另外附上彩蛋：GIF逐帧检测识别

CXK_dt2.gif

gif_detect.m

path = 'CXK2.gif';

for i=1:80 %取其中1-80帧

frame = imread(path,'gif','frame',i);

frame_edge = edge(frame,'roberts');

[I,map]=gray2ind(frame_edge,256);

if(i==1)

imwrite(I,map,'CXK_dt2.gif','DelayTime',0.1,'LoopCount',Inf)

else

imwrite(I,map,'CXK_dt2.gif','WriteMode','append','DelayTime',0.1)

end

end