This commit is contained in:
Maria Dimashova 2011-05-05 11:44:11 +00:00
parent 66c116ec6a
commit 95f7e5ca2f

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@ -192,7 +192,7 @@ void GMM::endLearning()
c[6] = prods[ci][2][0]/n - m[2]*m[0]; c[7] = prods[ci][2][1]/n - m[2]*m[1]; c[8] = prods[ci][2][2]/n - m[2]*m[2];
double dtrm = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]);
if( dtrm < std::numeric_limits<double>::epsilon() )
if( dtrm <= std::numeric_limits<double>::epsilon() )
{
// Adds the white noise to avoid singular covariance matrix.
c[0] += variance;
@ -213,7 +213,7 @@ void GMM::calcInverseCovAndDeterm( int ci )
double dtrm =
covDeterms[ci] = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]);
CV_Assert( dtrm > std::numeric_limits<double>::epsilon() );
CV_Assert( dtrm > std::numeric_limits<double>::epsilon() );
inverseCovs[ci][0][0] = (c[4]*c[8] - c[5]*c[7]) / dtrm;
inverseCovs[ci][1][0] = -(c[3]*c[8] - c[5]*c[6]) / dtrm;
inverseCovs[ci][2][0] = (c[3]*c[7] - c[4]*c[6]) / dtrm;