Find an interesting point in the detector detection algorithm

Question

I tried very hard. But I cannot find one point of interest in the SURF algorithm in Emgu CV. I wrote code for SURF. and I have problems that sometimes arise if the operator is next to my number "number 1", and sometimes it is not based on different images. Why is this so? based on the fact that homography is calculated as non-empty. how can i draw a circle or lines. who also have a problem. a circle or rectangle is drawn on a 0.0 point image. Please help me. I will be grateful.

public Image<Bgr, Byte> Draw(Image<Gray, byte> conditionalImage, Image<Gray, byte> observedImage, out long matchTime)
    {
        //observedImage = observedImage.Resize(, INTER.CV_INTER_LINEAR);
        Stopwatch watch;
        HomographyMatrix homography = null;

        SURFDetector surfCPU = new SURFDetector(500, false);
        VectorOfKeyPoint modelKeyPoints;
        VectorOfKeyPoint observedKeyPoints;
        Matrix<int> indices;

        Matrix<byte> mask;
        int k = 2;
        double uniquenessThreshold = 0.8;
            //extract features from the object image
            modelKeyPoints = surfCPU.DetectKeyPointsRaw(conditionalImage, null);

            Matrix<float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(conditionalImage, null, modelKeyPoints);

            watch = Stopwatch.StartNew();

            // extract features from the observed image
            observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
            Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
            BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
            matcher.Add(modelDescriptors);

            indices = new Matrix<int>(observedDescriptors.Rows, k);
            using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
            {
                matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
                mask = new Matrix<byte>(dist.Rows, 1);
                mask.SetValue(255);
                Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
            }

            int nonZeroCount = CvInvoke.cvCountNonZero(mask);

 //My Section number = 1
            if (nonZeroCount >= 4)
            {
                nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
                if (nonZeroCount >= 4)
                    homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
            }

            watch.Stop();

        //Draw the matched keypoints
            Image<Bgr, Byte> result = Features2DToolbox.DrawMatches(conditionalImage,     modelKeyPoints, observedImage, observedKeyPoints,
                indices, new Bgr(Color.Blue), new Bgr(Color.Red), mask,     Features2DToolbox.KeypointDrawType.DEFAULT);






        #region draw the projected region on the image
        if (homography != null)
        {  //draw a rectangle along the projected model
            Rectangle rect = conditionalImage.ROI;
            PointF[] pts = new PointF[] { 
           new PointF(rect.Left, rect.Bottom),
           new PointF(rect.Right, rect.Bottom),
           new PointF(rect.Right, rect.Top),
           new PointF(rect.Left, rect.Top)};
            homography.ProjectPoints(pts);
            PointF _circleCenter = new PointF();
            _circleCenter.X = (pts[3].X + ((pts[2].X - pts[3].X) / 2));
            _circleCenter.Y = (pts[3].Y + ((pts[0].Y - pts[3].Y) / 2));

            result.Draw(new CircleF(_circleCenter, 15), new Bgr(Color.Red), 10);
            result.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round),     true, new Bgr(Color.Cyan), 5);
        }
        #endregion

        matchTime = watch.ElapsedMilliseconds;

        return result;
    }

Answer 1

modelKeyPoints = surfCPU.DetectKeyPointsRaw(conditionalImage, null);

modelKeyPoints Image. .

, , . knn:

using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
{
    matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
    mask = new Matrix<byte>(dist.Rows, 1);
    mask.SetValue(255);
    Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}

2 (k) . 0,8 ( Threshold), , . , , : , , , .

.