看到OpenCV2.3.1裡面ORB特徵提取演算法也在裡面了，套用給的SURF特徵例子程式改為ORB特徵一直提示錯誤，型別不匹配神馬的，由於沒有找到示例程式，只能自己找答案。（ORB特徵論文：ORB： an efficient alternative to SIFT or SURF.點選下載論文）
經過查詢發現：
描述符資料型別有是float的，比如說SIFT，SURF描述符，還有是uchar的，比如說有ORB，BRIEF
對於float 匹配方式有：
FlannBased
BruteForce

BruteForceMatcher< L2<float> > matcher;//改動的地方
 

完整程式碼如下：

#include <iostream>  
#include "opencv2/core/core.hpp"  
#include "opencv2/features2d/features2d.hpp"  
#include "opencv2/highgui/highgui.hpp"  
#include <iostream>  
#include <vector>  
using namespace cv;  
using namespace std;  
int main()  
{  
    Mat img_1 = imread("D:\\image\\img1.jpg"
 
);  
    Mat img_2 = imread("D:\\image\\img2.jpg");  
    if (!img_1.data || !img_2.data)  
    {  
        cout << "error reading images " << endl;  
        return -1;  
    }  

    ORB orb;  
    vector<KeyPoint> keyPoints_1, keyPoints_2;  
    Mat descriptors_1, descriptors_2;  

    orb(img_1, Mat(), keyPoints_1, descriptors_1);  
    orb(img_2, Mat(), keyPoints_2, descriptors_2);  

    BruteForceMatcher<HammingLUT> matcher;  
    vector
 
<DMatch> matches;  
    matcher.match(descriptors_1, descriptors_2, matches);  

    double max_dist = 0; double min_dist = 100;  
    //-- Quick calculation of max and min distances between keypoints  
    for( int i = 0; i < descriptors_1.rows; i++ )  
    {   
        double dist = matches[i].distance;  
        if( dist < min_dist ) min_dist = dist;  
        if( dist > max_dist ) max_dist = dist;  
    }  
    printf("-- Max dist : %f \n", max_dist );  
    printf("-- Min dist : %f \n", min_dist );  
    //-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist )  
    //-- PS.- radiusMatch can also be used here.  
    std::vector< DMatch > good_matches;  
    for( int i = 0; i < descriptors_1.rows; i++ )  
    {   
        if( matches[i].distance < 0.6*max_dist )  
        {   
            good_matches.push_back( matches[i]);   
        }  
    }  

    Mat img_matches;  
    drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,  
        good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),  
        vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  
    imshow( "Match", img_matches);  
    cvWaitKey();  
    return 0;  
}  

另外: SURF SIFT

SIFT sift;
sift(img_1, Mat(), keyPoints_1, descriptors_1);
sift(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;

SURF surf;
surf(img_1, Mat(), keyPoints_1);
surf(img_2, Mat(), keyPoints_2);
SurfDescriptorExtractor extrator;
extrator.compute(img_1, keyPoints_1, descriptors_1);
extrator.compute(img_2, keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;

效果：

另外一個是尋找目標匹配
在右邊的場景圖裡面尋找左邊那幅圖的starbucks標誌

效果如下：

需要在之前的那個imshow之前加上如下程式碼即可完成一個簡單的功能展示：

// localize the object  
std::vector<Point2f> obj;  
std::vector<Point2f> scene;  

for (size_t i = 0; i < good_matches.size(); ++i)  
{  
    // get the keypoints from the good matches  
    obj.push_back(keyPoints_1[ good_matches[i].queryIdx ].pt);  
    scene.push_back(keyPoints_2[ good_matches[i].trainIdx ].pt);  
}  
Mat H = findHomography( obj, scene, CV_RANSAC );  

// get the corners from the image_1  
std::vector<Point2f> obj_corners(4);  
obj_corners[0] = cvPoint(0,0);  
obj_corners[1] = cvPoint( img_1.cols, 0);  
obj_corners[2] = cvPoint( img_1.cols, img_1.rows);  
obj_corners[3] = cvPoint( 0, img_1.rows);  
std::vector<Point2f> scene_corners(4);  

perspectiveTransform( obj_corners, scene_corners, H);  

// draw lines between the corners (the mapped object in the scene - image_2)  
line( img_matches, scene_corners[0] + Point2f( img_1.cols, 0), scene_corners[1] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[1] + Point2f( img_1.cols, 0), scene_corners[2] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[2] + Point2f( img_1.cols, 0), scene_corners[3] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[3] + Point2f( img_1.cols, 0), scene_corners[0] + Point2f( img_1.cols, 0),Scalar(0,255,0));