SIFT特征提取-應用篇

SIFT特征具有縮放、旋轉特征不變性，下載下傳了大牛的matlab版SIFT特征提取代碼，解釋如下：

1.調用方法：

将檔案加入matlab目錄後，在主程式中有兩種操作：

op1：尋找圖像中的Sift特征：

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

op2：對兩幅圖中的SIFT特征進行比對：

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

由于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特征檢測結果非常好。

2.代碼下載下傳位址：

http://www.cs.ubc.ca/~lowe/keypoints/      

3.想用自己的圖檔進行調用：      

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代碼：

// 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);//在記憶體中畫出特征點
	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代碼：

http://blog.csdn.net/abcjennifer/article/details/7639681

關于sift的其他講解:

http://blog.csdn.net/abcjennifer/article/details/7639681

http://blog.csdn.net/abcjennifer/article/details/7372880

http://blog.csdn.net/abcjennifer/article/details/7365882

關于computer vision的更多讨論與交流，敬請關注本部落格和新浪微網誌Sophia_qing。