冲浪功能提取

Question

目标：使用Surf descriptors和opencv 2.4.9库来匹配blob。

算法：基于以下链接：Steps

---

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

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
  if( argc != 3 )
  { readme(); 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 )
  { 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_1, keypoints_2;

  detector.detect( img_1, keypoints_1 );
  detector.detect( img_2, keypoints_2 );

  //-- Draw keypoints
  Mat img_keypoints_1; Mat img_keypoints_2;

  drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
  drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );

  //-- Show detected (drawn) keypoints
  imshow("Keypoints 1", img_keypoints_1 );
  imshow("Keypoints 2", img_keypoints_2 );

  waitKey(0);

  return 0;
  }

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

---

关键点检测的结果：在下图中，关键点的数量非常多，并且重要性不高。如何选择最能描述blob的关键点的最佳子集。除了冲浪之外还有更好的方法吗？这些Blob是二进制

Answer 1

较高的minHessian会产生较少的KeyPoints。

很难从图像中分辨出你想要匹配的两个输入图像是什么，你的目标究竟是什么（将匹配＆＃34; Vo＆＃34;＆＃34; Vos ..＆＃ 34;与＃34; Votre ......＆＃34;是成功还是失败？