Homography, inliers/ Emgu CV/ SURF in C#

Question

How can I get inliers/outliers of matched kyepoints using homography or some other method in C#?

I am working on SURF example provided on http://www.emgu.com/wiki/index.php/SURF_feature_detector_in_CSharp.

I got matchedFeature. Code uses HomographyMatrix (homography). I want to separate inliers and outliers.

In C++:

bgroup({findFundamentalMat})

int cvFindFundamentalMat(const CvMat* points1, const CvMat* points2, 
    CvMat* fundamentalMatrix, int method=CV_FM_RANSAC, double param1=1., 
    double param2=0.99, CvMat* status=NULL)

returns inliers. Can I see similiar code also in C#.

Again I just need outliers/inliers separation.

Answer 1

If you want inliers/outliers separation and you already have your matches try this:

//**RANSAC OUTLIER REMOVAL **//
Mat status;
vector<Point2f> trainMatches;
vector<Point2f> queryMatches;
vector<DMatch> inliers; 

    for( int i = 0; i < goodmatches.size(); i++ )
    {
        //-- Get the keypoints from the good matches
        trainMatches.push_back( cv::Point2f(keypointsB[ goodmatches[i].trainIdx ].pt.x/640.0f, keypointsB[ goodmatches[i].trainIdx ].pt.y/480.0f) );
        queryMatches.push_back( cv::Point2f(keypointsA[ goodmatches[i].queryIdx ].pt.x/640.0f, keypointsA[ goodmatches[i].queryIdx ].pt.y/480.0f) );
    }   

    Mat _homography;    
    Mat h = cv::findHomography(trainMatches,queryMatches,CV_RANSAC,0.005, status);

    for(size_t i = 0; i < queryMatches.size(); i++) 
    {
        if(status.at<char>(i) != 0) 
        {
            inliers.push_back(goodmatches[i]);
        }
    }

Note that I normalized the points so Homography estimation is more robust.

Answer 2

Your question is not so clear because if you are using emgucv homography computation is estimated using CameraCalibration.FindHomography() function using RANSAC if there are more than 10 matching pairs. I'm working on these topics for my thesis so i will post some relevant code that should fully reply to you and serve also to others.

result = MatchingRefinement.VoteForSizeAndOrientation(result, 1.5, 20);
homography = MatchingRefinement.
    GetHomographyMatrixFromMatchedFeatures(result, 
        HomographyDirection.DIRECT, HOMOGRAPHY_METHOD.LMEDS);
inverseHomography = MatchingRefinement.GetHomographyMatrixFromMatchedFeatures(
    result, HomographyDirection.INVERSE, HOMOGRAPHY_METHOD.LMEDS);

PointF[] pts1 = new PointF[result.Length];
PointF[] pts1_t = new PointF[result.Length];
PointF[] pts2 = new PointF[result.Length];

for (int i = 0; i < result.Length; i++)
{
    pts1[i] = result[i].ObservedFeature.KeyPoint.Point;
    pts1_t[i] = result[i].ObservedFeature.KeyPoint.Point;
    pts2[i] = result[i].SimilarFeatures[0].Feature.KeyPoint.Point;
}

// Project model features according to homography
homography.ProjectPoints(pts1_t);

Image<Bgr, Byte> finalCorrespondance = inputImage.Copy();

matchedInliersFeatures = new List<MatchedImageFeature>();

for (int i1 = 0; i1 < pts1_t.Length; i1++)
{
    if (Math.Sqrt(Math.Pow(pts2[i1].X - pts1_t[i1].X, 2d) + 
        Math.Pow(pts2[i1].Y - pts1_t[i1].Y, 2d)) <4d) // Inlier
    {
        PointF p_t = pts1_t[i1];
        PointF p = pts1[i1];
        finalCorrespondance.Draw(new CircleF(p, 2f), 
            new Bgr(Color.Yellow), 2);
        finalCorrespondance.Draw(new CircleF(p_t, 2f), 
            new Bgr(Color.Black), 2);
        finalCorrespondance.Draw(new LineSegment2DF(p, p_t), 
            new Bgr(Color.Blue), 1);

        MatchedImageFeature feature = new MatchedImageFeature();
        feature.SimilarFeatures = new SimilarFeature[] { 
            result[i1].SimilarFeatures[0] 
        };
        feature.ObservedFeature = result[i1].ObservedFeature;
        matchedInliersFeatures.Add(feature);
    }
}

List<ImageFeature> inliers = new List<ImageFeature>();
foreach (MatchedImageFeature match in matchedInliersFeatures)
{
    inliers.Add(match.ObservedFeature);
    inliers.Add(match.SimilarFeatures[0].Feature);
}