GAIJINBIANYUANJIANCE.zip_canny改进_改进 canny_改进canny_边缘检测_边缘检测改进

clear all %一、图像的预处理，读入彩色图像并将其灰度化 img=imread('预处理道路.jpg');%读入JPG彩色图像文件 %img=imnoise(img,'salt & pepper',0.02); %加噪声 0.02为参数表示噪声的大小 slope = zeros(341, 341); sub_img = zeros(5, 5); for m=2: 340 for n=2: 340 sub_img = img(m-1: m+1, n-1: n+1); imgsize = size(sub_img); N = imgsize(1); distance = zeros(1, N^4); difference = zeros(1, N^4); i = 1; for x1 = 1 : N for y1 = 1 : N for x2 = 1 : N for y2 = 1 : N dis = sqrt((x2-x1)^2 + (y2-y1)^2); if dis ~= 0 distance(i) = dis; difference(i) = abs(sub_img(x2,y2)-sub_img(x1,y1)); i = i + 1; end end end end end distance = distance(1:i-1); difference = difference(1:i-1); arr_size = size(distance); arr_size = arr_size(2); sr = zeros(1, arr_size); pn = zeros(1, arr_size); di_sum = zeros(1, arr_size); di = zeros(1, arr_size); [distance, pos]=sort(distance); difference = difference(pos); count = 1; for i = 1 : arr_size sr(count) = distance(i); pn(count) = pn(count) + 1; di_sum(count) = di_sum(count) + difference(i); if i ~= arr_size if sr(count) ~= distance(i+1) count = count + 1; end end end sr1 = sr(1:count); pn1 = pn(1:count); di_sum1 = di_sum(1:count); di1 = di(1:count); pn1 = pn1 / 2; di1 = di_sum1 ./ pn1; % log(sr) ~ log(di) log_sr = log2(sr1); log_di = log2(di1); p = polyfit(log_sr, log_di, 1); slope(m, n) = p(2); end end disp(slope); results = zeros(341, 341); for m=2: 340 for n=2: 340 %if slope(m, n) > 2.9 && slope(m, n) < 5.9 if slope(m, n) < 4.9 results(m, n) = 0; else results(m, n) = 255; end end end results = uint8(results); figure,imshow(results); % for m=2: 340 % for n=2: 340 % if img(m, n) < 110 % results(m, n) = 0; % else % results(m, n) = 255; % end % end % end % results = uint8(results); % figure,imshow(results); % img_2=medfilt2(results); % figure,imshow(img_2); % title('中值滤波后图像');