Feature extraction and detection 15-akaze local matching

AKAZE Local matching

    AKAZE Introduction to local matching

     Code demonstration

AKAZE Introduction to local matching

    KAZE Introduce

    AKAZE Introduce

AKAZE Introduction to local matching

    AOS Construct scale space

    Hessian Matrix feature point detection

     The direction assignment is based on the first-order differential image

     Descriptor generation

And SIFT、SUFR Compare

     A more stable

     Nonlinear scale space

    AKAZE Faster

     A relatively new algorithm , Only OpenCV Only the new version can be used

Code demonstration

    KAZE And AKAZE testing

#include <opencv2/opencv.hpp>
#include <iostream>


using namespace cv;
using namespace std;


int main(int argc, char** argv) {
    Mat src = imread("D:/vcprojects/images/test.png", IMREAD_GRAYSCALE);
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    imshow("input image", src);


    // kaze detection
    Ptr<AKAZE> detector = AKAZE::create();
    vector<KeyPoint> keypoints;
    double t1 = getTickCount();
    detector->detect(src, keypoints, Mat());
    double