SAD+MAD+SSD+MSD+NCC算法matlab实现代码

%% %基于NCC算法(Normalized Cross Correlation)的图像对准。 %% clear all; close all; clc; %1.读取图片 img_A_dir = '.\data\lena.bmp'; %待寻母图 img_A_raw = imread(img_A_dir); [r1,c1,d1] = size(img_A_raw); if d1==3 %灰度化 img_A = rgb2gray(img_A_raw); else img_A = img_A_raw; end img_B_dir = '.\data\refer.bmp'; %模板图 img_B_raw = imread(img_B_dir); [r2,c2,d2] = size(img_B_raw); if d2==3 img_B = rgb2gray(img_B_raw); else img_B = img_B_raw; end %2.计算NCC矩阵 mNCC = zeros(r1-r2,c1-c2); for i = 1:r1-r2 for j = 1:c1-c2 temp = img_A(i:i+r2-1,j:j+c2-1); mean_temp = mean(temp(:)); %temp均值 mean_B = mean(img_B(:)); %img_B均值 inp = sum(sum((temp - mean_temp).*(img_B - mean_B))); %两向量内积 mod1 = sqrt(sum(sum((temp - mean_temp).^2))); %模长1 mod2 = sqrt(sum(sum((img_B - mean_B).^2))); %模长2 ncc = inp / (mod1*mod2); mNCC(i,j) = mNCC(i,j) + ncc; end end %3.定位匹配位置 max_ncc = max(max(mNCC)); %最大ncc值 [x,y] = find(mNCC == max_ncc); x = x(1); %定位到的第一个位置 y = y(1); %4.保存结果图 getImg = img_A_raw(x:x+r2-1,y:y+c2-1,1:3); imwrite(getImg,'.\output\NCC_match.bmp'); fprintf('\n Done. \n');