改进特征点与OpenCV的匹配 [英] Improve matching of feature points with OpenCV

问题描述

我想在立体图像中匹配特征点。我已经找到和提取的特征点与不同的算法，现在我需要一个很好的匹配。在这种情况下，我使用FAST算法进行检测和提取，并使用 BruteForceMatcher 匹配特征点。

I want to match feature points in stereo images. I've already found and extracted the feature points with different algorithms and now I need a good matching. In this case I'm using the FAST algorithms for detection and extraction and the BruteForceMatcher for matching the feature points.

匹配代码：

vector< vector<DMatch> > matches;
//using either FLANN or BruteForce
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(algorithmName);
matcher->knnMatch( descriptors_1, descriptors_2, matches, 1 );

//just some temporarily code to have the right data structure
vector< DMatch > good_matches2;
good_matches2.reserve(matches.size());  
for (size_t i = 0; i < matches.size(); ++i)
{ 
    good_matches2.push_back(matches[i][0]);     
}

因为有很多错误匹配，我计算了最小和最大距离，删除所有太差的匹配：

Because there are a lot of false matches I caluclated the min and max distance and remove all matches that are too bad:

//calculation of max and min distances between keypoints
double max_dist = 0; double min_dist = 100;
for( int i = 0; i < descriptors_1.rows; i++ )
{
    double dist = good_matches2[i].distance;
    if( dist < min_dist ) min_dist = dist;
    if( dist > max_dist ) max_dist = dist;
}

//find the "good" matches
vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ )
{
    if( good_matches2[i].distance <= 5*min_dist )
    {
        good_matches.push_back( good_matches2[i]); 
    }
}

问题是，假匹配或只有几个正确的匹配（见下图）。

The problem is, that I either get a lot of false matches or only a few right ones (see the images below).

我认为这不是一个编程的问题，但更多的匹配的事情。据我所知， BruteForceMatcher 只考虑了特征点的视觉距离（存储在 FeatureExtractor 中）不是本地距离（x& y位置），这在我的情况下也很重要。有任何人有这个问题的经验或一个好主意，以提高匹配的结果？

I think it's not a problem of programming but more a matching thing. As far as I understood the BruteForceMatcher only regards the visual distance of feature points (which is stored in the FeatureExtractor), not the local distance (x&y position), which is in my case important, too. Has anybody any experiences with this problem or a good idea to improve the matching results?

EDIT

我改变了代码，它给了我50个最好的匹配。之后，我通过第一场比赛检查，无论是在一个指定的区域。

I changed the code, that it gives me the 50 best matches. After this I go through the first match to check, whether it's in a specified area. If it's not, I take the next match until I have found a match inside the given area.

vector< vector<DMatch> > matches;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(algorithmName);
matcher->knnMatch( descriptors_1, descriptors_2, matches, 50 );

//look if the match is inside a defined area of the image
double tresholdDist = 0.25 * sqrt(double(leftImageGrey.size().height*leftImageGrey.size().height + leftImageGrey.size().width*leftImageGrey.size().width));

vector< DMatch > good_matches2;
good_matches2.reserve(matches.size());  
for (size_t i = 0; i < matches.size(); ++i)
{ 
    for (int j = 0; j < matches[i].size(); j++)
    {
    //calculate local distance for each possible match
    Point2f from = keypoints_1[matches[i][j].queryIdx].pt;
    Point2f to = keypoints_2[matches[i][j].trainIdx].pt;        
    double dist = sqrt((from.x - to.x) * (from.x - to.x) + (from.y - to.y) * (from.y - to.y));
    //save as best match if local distance is in specified area
    if (dist < tresholdDist)
    {
        good_matches2.push_back(matches[i][j]);
        j = matches[i].size();
    }
}

我想我没有得到更多的匹配，我可以删除更多的假匹配：

I think I don't get more matches, but with this I'm able to remove more false matches:

推荐答案

通过比较所有的特征检测算法，我发现了一个很好的组合，现在我使用FAST的特征检测，SIFT的特征提取和BruteForce的匹配。结合检查，无论匹配是否在定义的区域内，我得到很多匹配，请参见图像：

By comparing all feature detection algorithms I found a good combination, which gives me a lot more matches. Now I am using FAST for feature detection, SIFT for feature extraction and BruteForce for the matching. Combined with the check, whether the matches is inside a defined region I get a lot of matches, see the image:

相关代码：

Ptr<FeatureDetector> detector;
detector = new DynamicAdaptedFeatureDetector ( new FastAdjuster(10,true), 5000, 10000, 10);
detector->detect(leftImageGrey, keypoints_1);
detector->detect(rightImageGrey, keypoints_2);

Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("SIFT");
extractor->compute( leftImageGrey, keypoints_1, descriptors_1 );
extractor->compute( rightImageGrey, keypoints_2, descriptors_2 );

vector< vector<DMatch> > matches;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
matcher->knnMatch( descriptors_1, descriptors_2, matches, 500 );

//look whether the match is inside a defined area of the image
//only 25% of maximum of possible distance
double tresholdDist = 0.25 * sqrt(double(leftImageGrey.size().height*leftImageGrey.size().height + leftImageGrey.size().width*leftImageGrey.size().width));

vector< DMatch > good_matches2;
good_matches2.reserve(matches.size());  
for (size_t i = 0; i < matches.size(); ++i)
{ 
    for (int j = 0; j < matches[i].size(); j++)
    {
        Point2f from = keypoints_1[matches[i][j].queryIdx].pt;
        Point2f to = keypoints_2[matches[i][j].trainIdx].pt;

        //calculate local distance for each possible match
        double dist = sqrt((from.x - to.x) * (from.x - to.x) + (from.y - to.y) * (from.y - to.y));

        //save as best match if local distance is in specified area and on same height
        if (dist < tresholdDist && abs(from.y-to.y)<5)
        {
            good_matches2.push_back(matches[i][j]);
            j = matches[i].size();
        }
    }
}

这篇关于改进特征点与OpenCV的匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！