本人在使用OpenCV SIFT特征提取算法时，遇到了问题，具体表现为 .exe触发了一个断点错误，经网上查找，发现是 vector 在析构时，造成了内存错误，解决方案由大神在csdn博客中给出，链接地址：

点击打开链接

按照大神所提示的 ，在使用opencv 函数之前，先对 vector 进行手动分配内存，分配内存后问题解决；

个人声明：本人在VS2013下调用sift算法，已经将问题解决，当然，具体问题，具体分析，本人适用，但不一定适用所有人，也希望大家对该问题的解决方案进行及时补充；

代码：

    initModule_nonfree();//初始化模块，使用SIFT或SURF时用到  

    //FeatureDetector::create()函数的参数可选
//     • "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");//创建SIFT特征检测器  

    //SiftFeatureDetector detector;
    //Ptr<FeatureDetector> detector = FeatureDetector::create("SURF");
//     DescriptorExtractor::create()函数的参数如下：
// 
//         • "SIFT" – SIFT
//         • "SURF" – SURF
//         • "BRIEF" – BriefDescriptorExtractor
//         • "BRISK" – BRISK
//         • "ORB" – ORB
//         • "FREAK" – FREAK
    //SiftDescriptorExtractor descriptor_extractor;
    Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create("SIFT");//创建特征向量生成器  
    //Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create("SURF");
// //     DescriptorMatcher::create()函数的参数如下，对应不同的匹配算法：
// //         – BruteForce(it uses L2)
// //         – BruteForce - L1
// //         – BruteForce - Hamming
// //         – BruteForce - Hamming(2)
// //         – FlannBased
    Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create("BruteForce");//创建特征匹配器,暴力匹配(简单匹配)  
//     if (detector.empty() || descriptor_extractor.empty())
//         cout << "fail to create detector!";

    //读入图像  
    Mat img1 = imread("001.jpg", 0);
    Mat img2 = imread("004.bmp", 0);

    if (img1.empty() || img1.channels() != 1)
        MessageBox(L"图像类型错误");
        //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)");

    //特征点检测  
    double t = getTickCount();//当前滴答数  
    vector<KeyPoint> keypoints1, keypoints2;
    keypoints1.resize(100);
    keypoints2.resize(100);
     detector->detect(img1, keypoints1);//检测img1中的SIFT特征点，存储到keypoints1中  
     detector->detect(img2, keypoints2);
    //detector.detect(img1, keypoints1);
    //detector.detect(img2, keypoints2);
//     cout << "图像1特征点个数:" << keypoints1.size() << endl;
//     cout << "图像2特征点个数:" << keypoints2.size() << endl;
// 
//     //根据特征点计算特征描述子矩阵，即特征向量矩阵  
    Mat descriptors1, descriptors2;
    descriptor_extractor->compute(img1, keypoints1, descriptors1);
    descriptor_extractor->compute(img2, keypoints2, descriptors2);
//     t = ((double)getTickCount() - t) / getTickFrequency();
//     cout << "SIFT算法用时：" << t << "秒" << endl;
// 
// 
//     cout << "图像1特征描述矩阵大小：" << descriptors1.size()
//         << "，特征向量个数：" << descriptors1.rows << "，维数：" << descriptors1.cols << endl;
//     cout << "图像2特征描述矩阵大小：" << descriptors2.size()
//         << "，特征向量个数：" << descriptors2.rows << "，维数：" << descriptors2.cols << endl;
// 
//     //画出特征点  
    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);

    //特征匹配  
    vector<DMatch> matches;//匹配结果  
    matches.resize(100);
    descriptor_matcher->match(descriptors1, descriptors2, matches);//匹配两个图像的特征矩阵  
    cout << "Match个数：" << matches.size() << endl;

    //计算匹配结果中距离的最大和最小值  
    //距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近  
    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 << "最大距离：" << max_dist << endl;
    cout << "最小距离：" << min_dist << endl;

    //筛选出较好的匹配点  
    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个数：" << goodMatches.size() << endl;

    //画出匹配结果  
    Mat img_matches;
    //红色连接的是匹配的特征点对，绿色是未匹配的特征点  
    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);