OpenCV SIFT特征学习 (三) 特征如何更好的匹配（上）

BruteForceMatcher与FlannBasedMatcher

OpenCV自带的匹配方式主要有两种，蛮力法BruteForceMatcher与FlannBasedMatcher
BruteForceMatcher
        匹配时，假设输入两组kyepoints为kp1,kp2，则按顺序kp1的点与kp2中的匹配，距离最近的即为匹配点，故匹配结果是产生数量与kp1相同的DMatch。距离的计算有以下方法：L1（绝对值相加，又称曼哈顿距离）、L2（欧几里得距离）、Hamming、HammingLUT
FlannBasedMatcher
        首先要理解Flann：FLANN（Fast
Approximate Nearest Neighbor Search Library，快速近似最近邻搜索库 ），是一个快速最近邻搜索优化算法的集合，用于大数据集以及高维数据的检索。介绍详见http://docs.opencv.org/modules/flann/doc/flann_fast_approximate_nearest_neighbor_search.html
​       代码如下（参考http://docs.opencv.org/doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.html）：

cv::Mat image1=cv::imread("D:\\sift1.bmp");

cv::Mat image2=cv::imread("D:\\sift2.bmp");

cv::Mat out;

cv::SiftFeatureDetector  siftdtc;

std::vector<cv::KeyPoint> kp1,kp2;

siftdtc.detect(image1,kp1);

siftdtc.detect(image2,kp2);

//---------------------------------------------------------

SiftDescriptorExtractor extractor;

Mat descriptor1,descriptor2;

Mat img_matches;

extractor.compute(image1,kp1,descriptor1);

extractor.compute(image2,kp2,descriptor2);

//-- Step 3: Matching descriptor vectors using FLANN matcher

FlannBasedMatcher matcher;

std::vector< DMatch > matches;

matcher.match( descriptor1, descriptor2, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints

for( int i = 0; i < descriptor1.rows; i++ )

{ double dist = matches[i].distance;

if( dist < min_dist ) min_dist = dist;

if( dist > max_dist ) max_dist = dist;

}

printf("-- Max dist : %f \n", max_dist );

printf("-- Min dist : %f \n", min_dist );

//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,

//-- or a small arbitary value ( 0.02 ) in the event that min_dist is very

//-- small)

//-- PS.- radiusMatch can also be used here.

std::vector< DMatch > good_matches;

for( int i = 0; i < descriptor1.rows; i++ )

{ if( matches[i].distance <= max(2*min_dist, 0.02) )

{ good_matches.push_back( matches[i]); }

}

//-- Draw only "good" matches

//Mat img_matches;

drawMatches( image1, kp1, image2, kp2,

good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),

vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

//-- Show detected matches

imshow( "Good Matches", img_matches );

for( int i = 0; i < (int)good_matches.size(); i++ )

{ printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }

cv::waitKey(0);      



由上图得，仍存在不少同名匹配点（即kp1与kp2不是1对1的关系），那么如何更好的筛选呢？常用的方法有随记采样一致算法（RANSAC）,详情请看下一章。