public Matrix<double> GetMatrix()
{
Matrix<double> mat = new DenseMatrix(RowCount, ColumnCount);
for(int ch = 0; ch < ChannelsCount; ++ch)
{
mat.PointwiseAddThis(ImageMatrix[ch]);
}
mat.MultiplyThis(1.0 / 3.0);
return mat;
}
public override void Match()
{
int r21 = 2 * WindowRadius + 1;
_leftMoments = new List<Vector<double>>();
_rightMoments = new List<Vector<double>>();
// Find circural mask bounds:
// for x = [-r,r] -> y = [ -sqrt(r^2 - x^2), sqrt(r^2 - x^2) ]
_ybounds = new int[r21];
for(int x = -WindowRadius; x <= WindowRadius; ++x)
{
_ybounds[x + WindowRadius] = (int)Math.Sqrt(WindowRadius * WindowRadius - x * x);
}
// Find moment vectors for each feature point
for(int i = 0; i < LeftFeaturePoints.Count; ++i)
{
IntVector2 pixel = LeftFeaturePoints[i];
if(pixel.X < WindowRadius || pixel.Y < WindowRadius ||
pixel.X >= LeftImage.ColumnCount - WindowRadius ||
pixel.Y >= LeftImage.RowCount - WindowRadius)
{
_leftMoments.Add(null);
}
else
{
Vector<double> mom = ComputeMomentVectorForPatch(pixel, LeftImage);
if(UseCenteredMoments)
mom = ComputeCenteredMomentVector(mom);
if(UseScaledMoments)
ScaleMomentVector(mom);
Vector<double> invMom = ComputeInvariantMomentVector(mom);
_leftMoments.Add(invMom);
}
}
for(int i = 0; i < RightFeaturePoints.Count; ++i)
{
IntVector2 pixel = RightFeaturePoints[i];
if(pixel.X < WindowRadius || pixel.Y < WindowRadius ||
pixel.X >= RightImage.ColumnCount - WindowRadius ||
pixel.Y >= RightImage.RowCount - WindowRadius)
{
_rightMoments.Add(null);
}
else
{
Vector<double> mom = ComputeMomentVectorForPatch(pixel, RightImage);
if(UseCenteredMoments)
mom = ComputeCenteredMomentVector(mom);
if(UseScaledMoments)
ScaleMomentVector(mom);
Vector<double> invMom = ComputeInvariantMomentVector(mom);
_rightMoments.Add(invMom);
}
}
// We need to find covariance matrix of invariants as they have
// different magnitudes, so simple ||Il - Ir|| may not be best choice
// E = 1/(n-1) sum( (xi-m)(xi-m)^T ) (x is column vector, m = 1/n sum(xi)
// 1) Find mean
int n = 0;
Vector<double> meanInv = new DenseVector(_invCount);
for(int i = 0; i < _leftMoments.Count; ++i)
{
if(_leftMoments[i] != null)
{
meanInv.PointwiseAddThis(_leftMoments[i]);
++n;
}
}
for(int i = 0; i < _rightMoments.Count; ++i)
{
if(_rightMoments[i] != null)
{
meanInv.PointwiseAddThis(_rightMoments[i]);
++n;
}
}
meanInv.MultiplyThis(1.0 / n);
// 2) Find E
Matrix<double> cov = new DenseMatrix(_invCount, _invCount);
for(int i = 0; i < _leftMoments.Count; ++i)
{
if(_leftMoments[i] != null)
{
cov.PointwiseAddThis(CamCore.MatrixExtensions.FromVectorProduct(_leftMoments[i] - meanInv));
}
}
for(int i = 0; i < _rightMoments.Count; ++i)
{
if(_rightMoments[i] != null)
{
cov.PointwiseAddThis(CamCore.MatrixExtensions.FromVectorProduct(_rightMoments[i] - meanInv));
}
}
cov.MultiplyThis(1.0 / (n - 1));
var covInv = cov.Inverse();
// Match each point pair and find ||Il - Ir||E
List<MatchedPair> costs;
var matchLeft = new List<MatchedPair>();
var matchRight = new List<MatchedPair>();
for(int l = 0; l < LeftFeaturePoints.Count; ++l)
{
costs = new List<MatchedPair>(LeftFeaturePoints.Count);
if(_leftMoments[l] != null)
{
for(int r = 0; r < RightFeaturePoints.Count; ++r)
{
if(_rightMoments[r] != null)
{
var d = _leftMoments[l] - _rightMoments[r];
costs.Add(new MatchedPair()
{
LeftPoint = new Vector2(LeftFeaturePoints[l]),
RightPoint = new Vector2(RightFeaturePoints[r]),
Cost = UseMahalanobis ? d * covInv * d : // cost = d^T * E^-1 * d
d.DotProduct(d)
});
}
}
costs.Sort((c1, c2) => { return c1.Cost > c2.Cost ? 1 : (c1.Cost < c2.Cost ? -1 : 0); });
// Confidence will be (c2-c1)/(c1+c2)
MatchedPair match = costs[0];
match.Confidence = (costs[1].Cost - costs[0].Cost) / (costs[1].Cost + costs[0].Cost);
matchLeft.Add(match);
}
}
for(int r = 0; r < RightFeaturePoints.Count; ++r)
{
costs = new List<MatchedPair>(RightFeaturePoints.Count);
if(_rightMoments[r] != null)
{
for(int l = 0; l < LeftFeaturePoints.Count; ++l)
{
if(_leftMoments[l] != null)
{
var d = _leftMoments[l] - _rightMoments[r];
costs.Add(new MatchedPair()
{
LeftPoint = new Vector2(LeftFeaturePoints[l]),
RightPoint = new Vector2(RightFeaturePoints[r]),
Cost = UseMahalanobis ? d * covInv * d : // cost = d^T * E^-1 * d
d.DotProduct(d)
});
}
}
costs.Sort((c1, c2) => { return c1.Cost > c2.Cost ? 1 : (c1.Cost < c2.Cost ? -1 : 0); });
// Confidence will be (c2-c1)/(c1+c2)
MatchedPair match = costs[0];
match.Confidence = (costs[1].Cost - costs[0].Cost) / (costs[1].Cost + costs[0].Cost);
matchRight.Add(match);
}
}
Matches = new List<MatchedPair>();
foreach(var ml in matchLeft)
{
MatchedPair mr = matchRight.Find((m) => { return ml.LeftPoint.DistanceTo(m.LeftPoint) < 0.01; });
// We have both sides matches
if(mr != null && ml.RightPoint.DistanceTo(mr.RightPoint) < 0.01)
{
// Cross check sucessful
Matches.Add(mr);
}
}
}