OpenCV中的SURF算法介绍

SURF：speed up robust feature，翻译为快速鲁棒特征。首先就其中涉及到的特征点和描述符做一些简单的介绍：

• 特征点和描述符　　

　　特征点分为两类：狭义特征点和广义特征点。狭义特征点的位置本身具有常规的属性意义，比如角点、交叉点等等。而广义特征点是基于区域定义的，它本身的位置不具备特征意义，只代表满足一定特征条件的特征区域的位置。广义特征点可以是某特征区域的任一相对位置。这种特征可以不是物理意义上的特征，只要满足一定的数学描述就可以，因而有时是抽象的。因此，从本质上说，广义特征点可以认为是一个抽象的特征区域，它的属性就是特征区域具备的属性；称其为点，是将其抽象为一个位置概念。

　　特征点既是一个点的位置标识，同时也说明它的局部邻域具有一定的模式特征。事实上，特征点是一个具有一定特征的局部区域的位置标识，称其为点，是将其抽象为一个位置概念，以便于确定两幅图像中同一个位置点的对应关系而进行图像匹配。所以在特征匹配过程中是以该特征点为中心，将邻域的局部特征进行匹配。也就是说在进行特征匹配时首先要为这些特征点（狭义和广义）建立特征描述，这种特征描述通常称之为描述符。 

　　一个好的特征点需要有一个好的描述方法将其表现出来，它涉及到的是图像匹配的一个准确性。因此在基于特征点的图像拼接和图像配准技术中，特征点和描述符同样重要。

更多内容可参考：http://blog.sina.com.cn/s/blog_4b146a9c0100rb18.html

• OpenCv中SURF的demo

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
  if( argc != 3 )
  { readme(); return -1; }

  Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

  if( !img_object.data || !img_scene.data )
  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }

  //-- Step 1: Detect the keypoints using SURF Detector
  int minHessian = 400;

  SurfFeatureDetector detector( minHessian );

  std::vector<KeyPoint> keypoints_object, keypoints_scene;

  detector.detect( img_object, keypoints_object );
  detector.detect( img_scene, keypoints_scene );

  //-- Step 2: Calculate descriptors (feature vectors)
  SurfDescriptorExtractor extractor;

  Mat descriptors_object, descriptors_scene;

  extractor.compute( img_object, keypoints_object, descriptors_object );
  extractor.compute( img_scene, keypoints_scene, descriptors_scene );

  //-- Step 3: Matching descriptor vectors using FLANN matcher
  FlannBasedMatcher matcher;
  std::vector< DMatch > matches;
  matcher.match( descriptors_object, descriptors_scene, matches );

  double max_dist = 0; double min_dist = 100;

  //-- Quick calculation of max and min distances between keypoints
  for( int i = 0; i < descriptors_object.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 
", max_dist );
  printf("-- Min dist : %f 
", min_dist );

  //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
  std::vector< DMatch > good_matches;

  for( int i = 0; i < descriptors_object.rows; i++ )
  { if( matches[i].distance < 3*min_dist )
     { good_matches.push_back( matches[i]); }
  }

  Mat img_matches;
  drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

  //-- Localize the object
  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_object[ good_matches[i].queryIdx ].pt );
    scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
  }

  Mat H = findHomography( obj, scene, CV_RANSAC );

  //-- Get the corners from the image_1 ( the object to be "detected" )
  std::vector<Point2f> obj_corners(4);
  obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
  obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.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_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
  line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
  line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
  line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

  //-- Show detected matches
  imshow( "Good Matches & Object detection", img_matches );

  waitKey(0);
  return 0;
  }

  /** @function readme */
  void readme()
  { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

有了对特征点和描述符的简单认识后，对上述代码就能有更好的理解了。

代码来源：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/feature_homography/feature_homography.html#feature-homography

• SURF算法的具体实现过程

整理了网上的一些资料：

1. surf算法原理,有一些简单介绍（1）

　　http://blog.csdn.net/andkobe/article/details/5778739

     2.  surf算法原理,有一些简单介绍（2）

  　 http://wuzizhang.blog.163.com/blog/static/78001208201138102648854/  

     3 . 特征点检测学习_2(surf算法)

      http://www.cnblogs.com/tornadomeet/archive/2012/08/17/2644903.html

• 其他

// DMatch function
DMatch(int queryIdx, int trainIdx, float distance)

其中 queryIdx 和 trainIdx 对应的特征点索引由match 函数决定，例如：

// 按如下顺序使用
match(descriptor_for_keypoints1, descriptor_for_keypoints2, matches)

则queryIdx 和 trainIdx分别对应keypoints1和keypoints2。

 2013-11-05