冲浪特征提取

Question

目标：通过使用Surf descriptors和opencv 2.4.9库匹配斑点。

算法：基于以下链接：Steps

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#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; } 

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结果关键点检测：在下面的图像的关键点的数目是非常高的，而不是许多是重要。如何选择最能描述blob的关键点的最佳子集。除Surf之外还有更好的方法吗？这些斑点是二进制

Answer 1

较高minHessian将产生较少的关键点。

很难从图像中分辨出你试图匹配的两个输入图像是什么，你的目标究竟是什么（将“Vos ..”的“Vo”与“Votre ...” “是成功还是失败？