SIFT特徵具有縮放、旋轉特徵不變性，下載了大牛的matlab版SIFT特徵提取程式碼，解釋如下：

1.呼叫方法：

將檔案加入matlab目錄後，在主程式中有兩種操作：

op1：尋找影象中的Sift特徵：

[image, descrips, locs] = sift('scene.pgm'); 
showkeys(image, locs);

match('scene.pgm','book.pgm');

由於scene和book兩圖中有相同的一本書，但orientation和size都不同，可以發現所得結果中Sift特徵檢測結果非常好。

2.程式碼下載地址：

3.想用自己的圖片進行呼叫：
 

i1=imread('D:\Images\New\Cars\image_0001.jpg');
i2=imread('D:\Images\New\Cars\image_0076.jpg');
i11=rgb2gray(i1);
i22=rgb2gray(i2);
imwrite(i11,'v1.jpg','quality',80);
imwrite(i22,'v2.jpg','quality',80);
match('v1.jpg','v2.jpg');

experiment results:

scene

book

compare result

EXP2:

C程式碼：

// FeatureDetector.cpp : Defines the entry point for the console application.
//

#include "stdafx.h"
#include "highgui.h"
#include "cv.h"
#include "vector"
#include "opencv\cxcore.hpp"
#include "iostream"
#include "opencv.hpp"
#include "nonfree.hpp"
#include "showhelper.h"

using namespace cv;
using namespace std;

int _tmain(int argc, _TCHAR* argv[])
{
	//Load Image 
	Mat c_src1 =  imread( "..\\Images\\3.jpg");
	Mat c_src2 = imread("..\\Images\\4.jpg");
	Mat src1 = imread( "..\\Images\\3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
	Mat src2 = imread( "..\\Images\\4.jpg", CV_LOAD_IMAGE_GRAYSCALE);
	if( !src1.data || !src2.data )
	{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }

	//sift feature detect
	SiftFeatureDetector detector;
	std::vector<KeyPoint> kp1, kp2;

	detector.detect( src1, kp1 );
	detector.detect( src2, kp2 );
	SiftDescriptorExtractor extractor;
	Mat des1,des2;//descriptor
	extractor.compute(src1,kp1,des1);
	extractor.compute(src2,kp2,des2);	
	Mat res1,res2; 
	int drawmode = DrawMatchesFlags::DRAW_RICH_KEYPOINTS;
	drawKeypoints(c_src1,kp1,res1,Scalar::all(-1),drawmode);//在記憶體中畫出特徵點
	drawKeypoints(c_src2,kp2,res2,Scalar::all(-1),drawmode);
	cout<<"size of description of Img1: "<<kp1.size()<<endl;
	cout<<"size of description of Img2: "<<kp2.size()<<endl;

	BFMatcher matcher(NORM_L2);
	vector<DMatch> matches;
	matcher.match(des1,des2,matches);
	Mat img_match;
	drawMatches(src1,kp1,src2,kp2,matches,img_match);//,Scalar::all(-1),Scalar::all(-1),vector<char>(),drawmode);
	cout<<"number of matched points: "<<matches.size()<<endl;
	imshow("matches",img_match);
	cvWaitKey();
	cvDestroyAllWindows();

	return 0;
}
 

Python程式碼：

關於sift的其他講解:

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