**在視頻質量診斷中，我們通常會涉及到“畫面抖動”的檢測。在此過程中就需要在視頻中隔N幀取一幀圖像，然后在獲取的兩幀圖像上找出特征點，并進行相應的匹配。** **當然了，這一過程中會出現很多的問題，例如：特征點失配等。** **本文主要關注特征點匹配及去除失配點的方法。** **主要功能：對統一物體拍了兩張照片，只是第二張圖片有選擇和尺度的變化。現在要分別對兩幅圖像提取特征點，然后將這些特征點匹配，使其盡量相互對應。** **下面，本文通過采用surf特征，分別使用Brute-force matcher和Flann-based matcher對特征點進行相互匹配。** **1、?BFMatcher?matcher** **第一段代碼摘自opencv官網的教程：** ``` #include "stdafx.h" #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/nonfree/features2d.hpp" #include "opencv2/calib3d/calib3d.hpp" #include "opencv2/imgproc/imgproc.hpp" using namespace cv; using namespace std; int _tmain(int argc, _TCHAR* argv[]) { Mat img_1 = imread( "haha1.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "haha2.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { return -1; } //-- Step 1: Detect the keypoints using SURF Detector //Threshold for hessian keypoint detector used in SURF int minHessian = 15000; SurfFeatureDetector detector( minHessian ); std::vector<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 ); //-- Step 2: Calculate descriptors (feature vectors) SurfDescriptorExtractor extractor; Mat descriptors_1, descriptors_2; extractor.compute( img_1, keypoints_1, descriptors_1 ); extractor.compute( img_2, keypoints_2, descriptors_2 ); //-- Step 3: Matching descriptor vectors with a brute force matcher BFMatcher matcher(NORM_L2,false); vector< DMatch > matches; matcher.match( descriptors_1, descriptors_2, matches ); //-- Draw matches Mat img_matches; drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches ); //-- Show detected matches imshow("Matches", img_matches ); waitKey(0); return 0; } ```