2、基本理論

高斯卷積函式定義為：

而原始影象與高斯卷積定義為：

因為：

所以Laplacian of Gaussian(LOG)可以通過先對高斯函式進行偏導操作，然後進行卷積求解。公式表示為：

和

因此，我們可以LOG核函式定義為

Laplacian of Gaussian計算可以利用高斯差分來近似，其中差分是由兩個高斯濾波與不同變數的卷積結果求得的。

#include<iostream>
#include<opencv2/opencv.hpp>
#include<features2d/features2d.hpp>
#include<opencv2/objdetect/objdetect.hpp>
#include"opencv2/imgproc/imgproc.hpp"
usingnamespace std;
usingnamespace cv;
intmain(intargc,char**argv)
{
       Matimg = imread("bear.jpg");
       if(img.empty())
       {
              std::cout<< "You didn't read the image sucessfully!" << std::endl;
              return0;
       }
       namedWindow("src");
       imshow("src", img);
       waitKey(20);
       MatimgGaussian,img16S,imgLoG,imgSobelx,imgSobely,imgSobel,imgCanny;
       GaussianBlur(img, imgGaussian,Size(3, 3),1);//高斯模糊
       namedWindow("Gaussian");
       imshow("Gaussian", imgGaussian);
       waitKey(0);
       Laplacian(imgGaussian, img16S, 3);
       convertScaleAbs(img16S,imgLoG,1);
       namedWindow("LoG");
       imshow("LoG", imgLoG);
       waitKey(0);
       Sobel(img, img16S, 3, 1, 0);
       convertScaleAbs(img16S, imgSobelx, 1);
       Sobel(img, img16S, 3, 0, 1);
       convertScaleAbs(img16S, imgSobely, 1);
       add(imgSobelx, imgSobely, imgSobel);
       namedWindow("Sobel");
       imshow("Sobel", imgSobel);
       waitKey(0);
       Canny(img, imgCanny, 100, 200);
       namedWindow("Canny");
       imshow("Canny", imgCanny);
       waitKey(0);
       return0;
}