本人在使用OpenCV SIFT特徵提取演算法時，遇到了問題，具體表現為 .exe觸發了一個斷點錯誤，經網上查詢，發現是 vector 在析構時，造成了記憶體錯誤，解決方案由大神在csdn部落格中給出，連結地址：

按照大神所提示的 ，在使用opencv 函式之前，先對 vector 進行手動分配記憶體，分配記憶體後問題解決；

個人宣告：本人在VS2013下呼叫sift演算法，已經將問題解決，當然，具體問題，具體分析，本人適用，但不一定適用所有人，也希望大家對該問題的解決方案進行及時補充；

程式碼：

	initModule_nonfree();//初始化模組，使用SIFT或SURF時用到  

	//FeatureDetector::create()函式的引數可選
// 	• "FAST" – FastFeatureDetector
// 		• "STAR" – StarFeatureDetector
// 		• "SIFT" – SIFT(nonfree module)
// 		• "SURF" – SURF(nonfree module)
// 		• "ORB" – ORB
// 		• "BRISK" – BRISK
// 		• "MSER" – MSER
// 		• "GFTT" – GoodFeaturesToTrackDetector
// 		• "HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled
// 		• "Dense" – DenseFeatureDetector
// 		• "SimpleBlob" – SimpleBlobDetector
	Ptr<FeatureDetector> detector = FeatureDetector::create("SIFT");//建立SIFT特徵檢測器  

	//SiftFeatureDetector detector;
	//Ptr<FeatureDetector> detector = FeatureDetector::create("SURF");
// 	DescriptorExtractor::create()函式的引數如下：
// 
// 		• "SIFT" – SIFT
// 		• "SURF" – SURF
// 		• "BRIEF" – BriefDescriptorExtractor
// 		• "BRISK" – BRISK
// 		• "ORB" – ORB
// 		• "FREAK" – FREAK
	//SiftDescriptorExtractor descriptor_extractor;
	Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create("SIFT");//建立特徵向量生成器  
	//Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create("SURF");
// // 	DescriptorMatcher::create()函式的引數如下，對應不同的匹配演算法：
// // 		– BruteForce(it uses L2)
// // 		– BruteForce - L1
// // 		– BruteForce - Hamming
// // 		– BruteForce - Hamming(2)
// // 		– FlannBased
	Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create("BruteForce");//建立特徵匹配器,暴力匹配(簡單匹配)  
// 	if (detector.empty() || descriptor_extractor.empty())
// 		cout << "fail to create detector!";

	//讀入影象  
	Mat img1 = imread("001.jpg", 0);
	Mat img2 = imread("004.bmp", 0);

	if (img1.empty() || img1.channels() != 1)
		MessageBox(L"影象型別錯誤");
		//CV_Error(Error::StsBadArg, "image is empty or has incorrect depth (!=CV_8U)");

	//if (!mask.empty() && mask.type() != CV_8UC1)
		//CV_Error(Error::StsBadArg, "mask has incorrect type (!=CV_8UC1)");

	//特徵點檢測  
	double t = getTickCount();//當前滴答數  
	vector<KeyPoint> keypoints1, keypoints2;
	keypoints1.resize(100);
	keypoints2.resize(100);
 

 	detector->detect(img1, keypoints1);//檢測img1中的SIFT特徵點，儲存到keypoints1中  
 	detector->detect(img2, keypoints2);
	//detector.detect(img1, keypoints1);
	//detector.detect(img2, keypoints2);
// 	cout << "影象1特徵點個數:" << keypoints1.size() << endl;
// 	cout << "影象2特徵點個數:" << keypoints2.size() << endl;
// 
// 	//根據特徵點計算特徵描述子矩陣，即特徵向量矩陣  
	Mat descriptors1, descriptors2;
	descriptor_extractor->compute(img1, keypoints1, descriptors1);
	descriptor_extractor->compute(img2, keypoints2, descriptors2);
// 	t = ((double)getTickCount() - t) / getTickFrequency();
// 	cout << "SIFT演算法用時：" << t << "秒" << endl;
// 
// 
// 	cout << "影象1特徵描述矩陣大小：" << descriptors1.size()
// 		<< "，特徵向量個數：" << descriptors1.rows << "，維數：" << descriptors1.cols << endl;
// 	cout << "影象2特徵描述矩陣大小：" << descriptors2.size()
// 		<< "，特徵向量個數：" << descriptors2.rows << "，維數：" << descriptors2.cols << endl;
// 
// 	//畫出特徵點  
	Mat img_keypoints1, img_keypoints2;
	drawKeypoints(img1, keypoints1, img_keypoints1, Scalar::all(-1), 0);
	drawKeypoints(img2, keypoints2, img_keypoints2, Scalar::all(-1), 0);
	imshow("Src1",img_keypoints1);  
	imshow("Src2",img_keypoints2); 
 	waitKey(0);

	//特徵匹配  
	vector<DMatch> matches;//匹配結果  
	matches.resize(100);
	descriptor_matcher->match(descriptors1, descriptors2, matches);//匹配兩個影象的特徵矩陣  
	cout << "Match個數：" << matches.size() << endl;

	//計算匹配結果中距離的最大和最小值  
	//距離是指兩個特徵向量間的歐式距離，表明兩個特徵的差異，值越小表明兩個特徵點越接近  
	double max_dist = 0;
	double min_dist = 100;
	for (int i = 0; i < matches.size(); i++)
	{
		double dist = matches[i].distance;
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}
	cout << "最大距離：" << max_dist << endl;
	cout << "最小距離：" << min_dist << endl;

	//篩選出較好的匹配點  
	vector<DMatch> goodMatches;
	for (int i = 0; i < matches.size(); i++)
	{
		if (matches[i].distance < 0.5 * max_dist)
		{
			goodMatches.push_back(matches[i]);
		}
	}
	cout << "goodMatch個數：" << goodMatches.size() << endl;

	//畫出匹配結果  
	Mat img_matches;
	//紅色連線的是匹配的特徵點對，綠色是未匹配的特徵點  
	drawMatches(img1, keypoints1, img2, keypoints2, goodMatches, img_matches,
		/*Scalar::all(-1)/ **/CV_RGB(255,0,0), CV_RGB(0, 255, 0), Mat(), 2);
	
	namedWindow("MatchSIFT", 0);
	imshow("MatchSIFT", img_matches);
	waitKey(0);