基于蚁群算法的图像边缘检测 MATLAB源程序.rar_matlab源程序_图像边缘检测_蚁群 边缘_蚁群算法 图像

function edge_ACO %参考文献："An Ant Colony Optimization Algorithm For Image Edge close all; clear all; clc; % 读入图像 filename = 'ant128'; raw=imread('ant.jpg'); img=rgb2gray(raw); img = double(img)./255; [nrow, ncol] = size(img); subplot(2,3,1); imshow(raw); %显示源图像 %公式（3.24.4）初始化 for nMethod = 2:5; %四种不同的核函数, 参见式 (3.24.7)-(3.24.10) %E: exponential; F: flat; G: gaussian; S:Sine; T:Turkey; W:Wave fprintf('Image edge detection using ant colony.\nPlease wait......\n'); v = zeros(size(img)); v_norm = 0; for rr =1:nrow for cc=1:ncol %定义像素团 temp1 = [rr-2 cc-1; rr-2 cc+1; rr-1 cc-2; rr-1 cc-1; rr-1 cc; rr-1 cc+1; rr-1 cc+2; rr cc-1]; temp2 = [rr+2 cc+1; rr+2 cc-1; rr+1 cc+2; rr+1 cc+1; rr+1 cc; rr+1 cc-1; rr+1 cc-2; rr cc+1]; temp0 = find(temp1(:,1)>=1 & temp1(:,1)<=nrow & temp1(:,2)>=1 & temp1(:,2)<=ncol & temp2(:,1)>=1 & temp2(:,1)<=nrow & temp2(:,2)>=1 & temp2(:,2)<=ncol); temp11 = temp1(temp0, :); temp22 = temp2(temp0, :); temp00 = zeros(size(temp11,1)); for kk = 1:size(temp11,1) temp00(kk) = abs(img(temp11(kk,1), temp11(kk,2))-img(temp22(kk,1), temp22(kk,2))); end if size(temp11,1) == 0 v(rr, cc) = 0; v_norm = v_norm + v(rr, cc); else lambda = 10; switch nMethod case 1%'F' temp00 = lambda .* temp00; case 2%'Q' temp00 = lambda .* temp00.^2; case 3%'S' temp00 = sin(pi .* temp00./2./lambda); case 4%'W' temp00 = sin(pi.*temp00./lambda).*pi.*temp00./lambda; end v(rr, cc) = sum(sum(temp00.^2)); v_norm = v_norm + v(rr, cc); end end end % 归一化 v = v./v_norm; v = v.*100; p = 0.0001 .* ones(size(img)); % 信息素函数初始化 %参数设置。 alpha = 1; %式（3.24.4）中的参数 beta = 0.1; %式（3.24.4）中的参数 rho = 0.1; %式（3.24.11）中的参数 %式（3.24.12）中的参数 phi = 0.05; %equation (12), i.e., (9) in IEEE-CIM-06 ant_total_num = round(sqrt(nrow*ncol)); % 记录蚂蚁的位置 ant_pos_idx = zeros(ant_total_num, 2); % 初始化蚂蚁的位置 rand('state', sum(clock)); temp = rand(ant_total_num, 2); ant_pos_idx(:,1) = round(1 + (nrow-1) * temp(:,1)); %行坐标 ant_pos_idx(:,2) = round(1 + (ncol-1) * temp(:,2)); %列坐标 search_clique_mode = '8'; %Figure 1 % 定义存储空间容量 if nrow*ncol == 128*128 A = 40; memory_length = round(rand(1).*(1.15*A-0.85*A)+0.85*A); elseif nrow*ncol == 256*256 A = 30; memory_length = round(rand(1).*(1.15*A-0.85*A)+0.85*A); elseif nrow*ncol == 512*512 A = 20; memory_length = round(rand(1).*(1.15*A-0.85*A)+0.85*A); end ant_memory = zeros(ant_total_num, memory_length); % 实施算法 if nrow*ncol == 128*128 % 迭代的次数 total_step_num = 300; elseif nrow*ncol == 256*256 total_step_num = 900; elseif nrow*ncol == 512*512 total_step_num = 1500; end total_iteration_num = 3; for iteration_idx = 1: total_iteration_num delta_p = zeros(nrow, ncol); for step_idx = 1: total_step_num delta_p_current = zeros(nrow, ncol); for ant_idx = 1:ant_total_num ant_current_row_idx = ant_pos_idx(ant_idx,1); ant_current_col_idx = ant_pos_idx(ant_idx,2); % 找出当前位置的邻域 if search_clique_mode == '4' rr = ant_current_row_idx; cc = ant_current_col_idx; ant_search_range_temp = [rr-1 cc; rr cc+1; rr+1 cc; rr cc-1]; elseif search_clique_mode == '8' rr = ant_current_row_idx; cc = ant_current_col_idx; ant_search_range_temp = [rr-1 cc-1; rr-1 cc; rr-1 cc+1; rr cc-1; rr cc+1; rr+1 cc-1; rr+1 cc; rr+1 cc+1]; end %移除图像外的位置 temp = find(ant_search_range_temp(:,1)>=1 & ant_search_range_temp(:,1)<=nrow & ant_search_range_temp(:,2)>=1 & ant_search_range_temp(:,2)<=ncol); ant_search_range = ant_search_range_temp(temp, :); %计算概率转换函数 ant_transit_prob_v = zeros(size(ant_search_range,1),1); ant_transit_prob_p = zeros(size(ant_search_range,1),1); for kk = 1:size(ant_search_range,1) temp = (ant_search_range(kk,1)-1)*ncol + ant_search_range(kk,2); if length(find(ant_memory(ant_idx,:)==temp))==0 ant_transit_prob_v(kk) = v(ant_search_range(kk,1), ant_search_range(kk,2)); ant_transit_prob_p(kk) = p(ant_search_range(kk,1), ant_search_range(kk,2)); else ant_transit_prob_v(kk) = 0; ant_transit_prob_p(kk) = 0; end end if (sum(sum(ant_transit_prob_v))==0) | (sum(sum(ant_transit_prob_p))==0) for kk = 1:size(ant_search_range,1) temp = (ant_search_range(kk,1)-1)*ncol + ant_search_range(kk,2); ant_transit_prob_v(kk) = v(ant_search_range(kk,1), ant_search_range(kk,2)); ant_transit_prob_p(kk) = p(ant_search_range(kk,1), ant_search_range(kk,2)); end end ant_transit_prob = (ant_transit_prob_v.^alpha) .* (ant_transit_prob_p.^beta) ./ ((sum(sum((ant_transit_prob_v.^alpha) .* (ant_transit_prob_p.^beta))))+eps); % 产生一个随机数来确定下一个位置 rand('state', sum(100*clock)); temp = find(cumsum(ant_transit_prob)>=rand(1), 1); ant_next_row_idx = ant_search_range(temp,1); ant_next_col_idx = ant_search_range(temp,2); if length(ant_next_row_idx) == 0 ant_next_row_idx = ant_current_row_idx; ant_next_col_idx = ant_current_col_idx; end ant_pos_idx(ant_idx,1) = ant_next_row_idx; ant_pos_idx(ant_idx,2) = ant_next_col_idx; delta_p_current(ant_pos_idx(ant_idx,1), ant_pos_idx(ant_idx,2)) = 1; if step_idx <= memory_length ant_memory(ant_idx,step_idx) = (ant_pos_idx(ant_idx,1)-1)*ncol + ant_pos_idx(ant_idx,2); elseif step_idx > memory_length ant_memory(ant_idx,:) = circshift(ant_memory(ant_idx,:),[0 -1]); ant_memory(ant_idx,end) = (ant_pos_idx(ant_idx,1)-1)*ncol + ant_pos_idx(ant_idx,2); end %更新信息素函数 p = ((1-rho).*p + rho.*delta_p_current.*v).*delta_p_current + p.*(abs(1-delta_p_current)); end delta_p = (delta_p + (delta_p_current>0))>0; p = (1-phi).*p; end end % 产生边缘图矩阵，运用信息素函数判断是否是边缘 % 调用子函数进行二值分割 T = func_seperate_two_class(p); %imwrite(uint8(abs((p>=T).*255-255)), gray(256), [filename '_edge_aco_' num2str(nMethod) '.jpg'], 'jpg'); subplot(2,3,nMethod) imshow(uint8(abs((p>=T).*255-255))); fprintf('Done!\n'); end %%%%%%%%子函数%%%%%%% function level = func_seperate_two_class(I) % 功能：进行二值分割 I = I(:); % STEP 1: 通过直方图计算灰度均值, 设定T=mean(I) [counts, N]=hist(I,256); i=1; mu=cumsum(counts); T(i)=(sum(N.*counts))/mu(end); % STEP 2: