基于特征点的图像匹配是图像处理中经常会遇到的问题，手动选取特征点太麻烦了。比较经典常用的特征点自动提取的办法有Harris特征、SIFT特征、SURF特征。

先介绍利用SURF特征的特征描述办法，其操作封装在类SurfFeatureDetector中，利用类内的detect函数可以检测出SURF特征的关键点，保存在vector容器中。第二部利用SurfDescriptorExtractor类进行特征向量的相关计算。将之前的vector变量变成向量矩阵形式保存在Mat中。最后强行匹配两幅图像的特征向量，利用了类BruteForceMatcher中的函数match。代码如下：

    /** 
     * @file SURF_descriptor 
     * @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions 
     * @author A. Huaman 
     */  
      
    #include <stdio.h>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"  
    //#include "opencv2/features2d/features2d.hpp"  
    #include "opencv2/highgui/highgui.hpp"  
    #include "opencv2/highgui/highgui.hpp"
    #include<opencv2/legacy/legacy.hpp>

    using namespace cv;  
      
    void readme();  
      
    /** 
     * @function main 
     * @brief Main function 
     */  
    int main( int argc, char** argv )  
    {  
      if( argc != 3 )  
      { return -1; }  
      
      Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );  
      Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );  
        
      if( !img_1.data || !img_2.data )  
      { return -1; }  
      
      //-- Step 1: Detect the keypoints using SURF Detector  
      int minHessian = 400;  
      
      SurfFeatureDetector detector( minHessian );  
      
      std::vector<KeyPoint> keypoints_1, keypoints_2;  
      
      detector.detect( img_1, keypoints_1 );  
      detector.detect( img_2, keypoints_2 );  
      
      //-- Step 2: Calculate descriptors (feature vectors)  
      SurfDescriptorExtractor extractor;  
      
      Mat descriptors_1, descriptors_2;  
      
      extractor.compute( img_1, keypoints_1, descriptors_1 );  
      extractor.compute( img_2, keypoints_2, descriptors_2 );  
      
      //-- Step 3: Matching descriptor vectors with a brute force matcher  
      BruteForceMatcher< L2<float> > matcher;  
      std::vector< DMatch > matches;  
      matcher.match( descriptors_1, descriptors_2, matches );  
      
      //-- Draw matches  
      Mat img_matches;  
      drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );   
      
      //-- Show detected matches  
      imshow("Matches", img_matches );  
      
      waitKey(0);  
      
      return 0;  
    }  
      
    /** 
     * @function readme 
     */  
    void readme()  
    { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }  

当然，进行强匹配的效果不够理想，这里再介绍一种FLANN特征匹配算法。前两步与上述代码相同，第三步利用FlannBasedMatcher类进行特征匹配，并只保留好的特征匹配点，代码如下：

    //-- Step 3: Matching descriptor vectors using FLANN matcher  
    FlannBasedMatcher matcher;  
    std::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 2*min_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 < 2*min_dist )  
      { good_matches.push_back( matches[i]); }  
    }    
      
    //-- Draw only "good" matches  
    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 );   
      
    //-- Show detected matches  
    imshow( "Good Matches", img_matches );  

在FLANN特征匹配的基础上，还可以进一步利用Homography映射找出已知物体。具体来说就是利用findHomography函数利用匹配的关键点找出相应的变换，再利用perspectiveTransform函数映射点群。具体代码如下：

    //-- Localize the object from img_1 in img_2   
    std::vector<Point2f> obj;  
    std::vector<Point2f> scene;  
      
    for( int 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 ( the object to be "detected" )  
    Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };  
    Point scene_corners[4];  
      
    //-- Map these corners in the scene ( image_2)  
    for( int i = 0; i < 4; i++ )  
    {  
      double x = obj_corners[i].x;   
      double y = obj_corners[i].y;  
      
      double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );  
      double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;  
      double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;  
      scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );  
    }    
       
    //-- Draw lines between the corners (the mapped object in the scene - image_2 )  
    line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );  
    line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );  
    line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );  
    line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 );  
      
    //-- Show detected matches  
    imshow( "Good Matches & Object detection", img_matches );  

然后再看一下Harris特征检测，在计算机视觉中，通常需要找出两帧图像的匹配点，如果能找到两幅图像如何相关，就能提取出两幅图像的信息。我们说的特征的最大特点就是它具有唯一可识别这一特点，图像特征的类型通常指边界、角点（兴趣点）、斑点（兴趣区域）。角点就是图像的一个局部特征，应用广泛。harris角点检测是一种直接基于灰度图像的角点提取算法，稳定性高，尤其对L型角点检测精度高，但由于采用了高斯滤波，运算速度相对较慢，角点信息有丢失和位置偏移的现象，而且角点提取有聚簇现象。具体实现就是使用函数cornerHarris实现。

除了利用Harris进行角点检测，还可以利用Shi-Tomasi方法进行角点检测。使用函数goodFeaturesToTrack对角点进行检测，效果也不错。也可以自己制作角点检测的函数，需要用到cornerMinEigenVal函数和minMaxLoc函数，最后的特征点选取，判断条件要根据自己的情况编辑。如果对特征点，角点的精度要求更高，可以用cornerSubPix函数将角点定位到子像素。

到群里问，大家也刚用2.4.2不久，只有个达人告诉我是头文件的问题。我试着去找了一下，发现2.3.1的include文件和2.4.2的差别很大，新版本里多了很多东西，对比了一下features2d.hpp这个文件，发现原先包含在features2d.hpp的BruteForceMatcher现在根本不在features2d.hpp中，我试着去查找其他有可能在的hpp文件，找了几个，发现这不是解决问题的办法，问google吧！在搜了一下之后发现，还真有人碰到了这个问题，也确实是头文件的问题：缺少了#include<opencv2/legacy/legacy.hpp>，加了之后，发现之前的错误确实没了。

但是新的问题出来了，说link出现问题，没经验的我还是只能问google（谷歌确实强大啊！！！），牛人一针见血的指出了问题所在：For those who would have the same problem, make sure you have ALL the right linker inputs (configuration -> linker -> inputs), included dlls such as opencv, highgui etc.在右键“属性”->"链接器"->“输入”->"附加依赖项"把新输入的legacy的静态文件opencv_legacy242d.lib加进来就ok了！

http://yangshen998.iteye.com/blog/1311575