Matlab实现图像边缘检测

function output = my_edge(input_img,method)
if size(input_img,3)==3
    input_img=rgb2gray(input_img);
end

input_img=im2double(input_img);
sobel_x=[-1,-2,-1;0,0,0;1,2,1];
sobel_y=[-1,0,1;-2,0,2;-1,0,1];
prewitt_x=[-1,-1,-1;0,0,0;1,1,1];
prewitt_y=[-1,0,1;-1,0,1;-1,0,1];

psf=fspecial('gaussian',[5,5],1);
input_img=imfilter(input_img,psf);%高斯低通滤波，平滑图像,但可能会使图像丢失细节
input_img=medfilt2(input_img); %中值滤波消除孤立点
[m,n]=size(input_img);
output=zeros(m,n);
if nargin==2
    if strcmp(method,'sobel')
        for i=2:m-1
            for j=2:n-1
                local_img=input_img(i-1:i+1, j-1:j+1);
%近似边缘检测，加快速度    %output(i,j)=abs(sum(sum(sobel_x.*local_img)))+abs(sum(sum(sobel_x.*local_img)));
                output(i,j)=sqrt(sum(sum(sobel_x.*local_img))^2+sum(sum(sobel_y.*local_img))^2);
            end
        end
    elseif strcmp(method,'prewitt')
          for i=2:m-1
            for j=2:n-1
                local_img=input_img(i-1:i+1, j-1:j+1);
                output(i,j)=sqrt(sum(sum(prewitt_x.*local_img))^2+sum(sum(prewitt_y.*local_img))^2);
            end
          end
    else
        errordlg('maybe you should input sobel or prewitt');
    end
else  %如果不输入算子的名称，默认使用roberts算子进行边缘检测
    for i=1:m-1
        for j=1:n-1
            output(i,j)=abs(input_img(i,j)-input_img(i+1,j+1))+ ...
                abs(input_img(i+1,j)-input_img(i,j+1));
        end
    end
end

output=imadjust(output);%使边缘图像更明显
thresh=graythresh(output);%确定二值化阈值
output=bwmorph(im2bw(output,thresh),'thin',inf);%强化细节
end