沖浪特征提取

Question

目標：通過使用Surf descriptors和opencv 2.4.9庫來匹配​​blob。

算法：基於以下鏈接： 步驟

---

#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; }

---

關鍵點檢測的結果：在下圖中，關鍵點的數量非常多，但並不重要。 如何選擇最能描述斑點的最佳關鍵點子集。 除了沖浪，還有其他更好的方法嗎？ 這些Blob是二進制的

Answer 1

較高的minHessian將產生較少的KeyPoint。

很難從圖像中分辨出您要匹配的兩個輸入圖像是什么，以及您的目標到底是什么（將“ Vos ..”的“ Vo”與“ Votre ...”的“ Vo”匹配將是成功的）還是失敗？