AbstractStructureTensorIPD selDifferentiationScaleFast(
FImage img, AbstractStructureTensorIPD ipd, float si, Pixel c) {
AbstractStructureTensorIPD best = ipd.clone();
float s = 0.75f;
float sigma;
FImage L;
L = img.clone();
float sd = s * si;
// Smooth previous smoothed image L
sigma = sd;
L.processInplace(new FGaussianConvolve(sigma, 3));
// X and Y derivatives
best.setDetectionScale(sd);
best.setIntegrationScale(si);
best.setImageBlurred(true);
best.findInterestPoints(L);
// M = calcSecondMomentMatrix(best, c.x, c.y);
// EigenValueVectorPair meig = MatrixUtils.symmetricEig2x2(M);
// Matrix eval = meig.getD();
// double eval1 = Math.abs(eval.get(0, 0));
// double eval2 = Math.abs(eval.get(1, 1));
return best;
}
/*
* Selects diffrentiation scale
*/
AbstractStructureTensorIPD selDifferentiationScale(
FImage img, AbstractStructureTensorIPD ipdToUse, float si, Pixel c) {
AbstractStructureTensorIPD best = null;
float s = 0.5f;
float sigma_prev = 0, sigma;
FImage L;
double qMax = 0;
L = img.clone();
AbstractStructureTensorIPD ipd = ipdToUse.clone();
while (s <= 0.751) {
Matrix M;
float sd = s * si;
// Smooth previous smoothed image L
sigma = (float) Math.sqrt(Math.pow(sd, 2) - Math.pow(sigma_prev, 2));
L.processInplace(new FGaussianConvolve(sigma, 3));
sigma_prev = sd;
// X and Y derivatives
ipd.setDetectionScale(sd);
ipd.setIntegrationScale(si);
ipd.setImageBlurred(true);
ipd.findInterestPoints(L);
// FImage Lx = L.process(new FConvolution(DX_KERNEL.multiply(sd)));
// FImage Ly = L.process(new FConvolution(DY_KERNEL.multiply(sd)));
//
// FGaussianConvolve gauss = new FGaussianConvolve(si, 3);
//
// FImage Lxm2 = Lx.multiply(Lx);
// dx2 = Lxm2.process(gauss);
//
// FImage Lym2 = Ly.multiply(Ly);
// dy2 = Lym2.process(gauss);
//
// FImage Lxmy = Lx.multiply(Ly);
// dxy = Lxmy.process(gauss);
M = calcSecondMomentMatrix(ipd, c.x, c.y);
// calc eigenvalues
// EigenvalueDecomposition meig = M.eig();
EigenValueVectorPair meig = MatrixUtils.symmetricEig2x2(M);
Matrix eval = meig.getValues();
double eval1 = Math.abs(eval.get(0, 0));
double eval2 = Math.abs(eval.get(1, 1));
double q = Math.min(eval1, eval2) / Math.max(eval1, eval2);
if (q >= qMax) {
qMax = q;
best = ipd.clone();
}
s += 0.05;
}
return best;
}