opencv 中自带的模板匹配算法出处

方法如下：
使用OPENCV下SIFT库做图像匹配的例程
// opencv_empty_proj.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;

int _tmain(int argc, _TCHAR* argv[])
{
const char* imagename = "img.jpg";

//从文件中读入图像
Mat img = imread(imagename);
Mat img2=imread("img2.jpg");

//如果读入图像失败
if(img.empty())
{
fprintf(stderr, "Can not load image %s\n", imagename);
return -1;
}
if(img2.empty())
{
fprintf(stderr, "Can not load image %s\n", imagename);
return -1;
}
//显示图像
imshow("image before", img);
imshow("image2 before",img2);
//sift特征检测
SiftFeatureDetector siftdtc;
vectorkp1,kp2;
siftdtc.detect(img,kp1);
Mat outimg1;
drawKeypoints(img,kp1,outimg1);
imshow("image1 keypoints",outimg1);
KeyPoint kp;
vector::iterator itvc;
for(itvc=kp1.begin();itvc!=kp1.end();itvc++)
{
cout<<"angle:"<angle<<"\t"<class_id<<"\t"<octave<<"\t"<pt<<"\t"<response< }
siftdtc.detect(img2,kp2);
Mat outimg2;
drawKeypoints(img2,kp2,outimg2);
imshow("image2 keypoints",outimg2);
SiftDescriptorExtractor extractor;
Mat descriptor1,descriptor2;
BruteForceMatcher> matcher;
vector matches;
Mat img_matches;
extractor.compute(img,kp1,descriptor1);
extractor.compute(img2,kp2,descriptor2);
imshow("desc",descriptor1);
cout< matcher.match(descriptor1,descriptor2,matches);
drawMatches(img,kp1,img2,kp2,matches,img_matches);
imshow("matches",img_matches);
//此函数等待按键，按键盘任意键就返回
waitKey();
return 0;
}

　　

1984.A Goshtasby et al."A Two-Stage Cross Correlation Approach to Template Matching"

2001.K Briechle, UD Hanebeck"Template Matching using Fast Normalized Cross Correlation"

论文电子版在附件里。