/**
* Use the feature detector to find candidate features in each level. Only compute the needed
* image derivatives.
*/
private void detectCandidateFeatures(GaussianScaleSpace<T, D> ss, double scale) {
// adjust corner intensity threshold based upon the current scale factor
float scaleThreshold = (float) (baseThreshold / Math.pow(scale, scalePower));
detector.setThreshold(scaleThreshold);
D derivX = null, derivY = null;
D derivXX = null, derivYY = null, derivXY = null;
if (detector.getRequiresGradient()) {
derivX = ss.getDerivative(true);
derivY = ss.getDerivative(false);
}
if (detector.getRequiresHessian()) {
derivXX = ss.getDerivative(true, true);
derivYY = ss.getDerivative(false, false);
derivXY = ss.getDerivative(true, false);
}
T image = ss.getScaledImage();
detector.process(image, derivX, derivY, derivXX, derivYY, derivXY);
List<Point2D_I16> m = maximums[spaceIndex];
m.clear();
QueueCorner q = detector.getFeatures();
for (int i = 0; i < q.size; i++) {
m.add(q.get(i).copy());
}
}
/**
* Create a feature detector.
*
* @param detector Point feature detector which is used to find candidates in each scale level
* @param scalePower Used to adjust feature intensity in each level to make them comparable.
* Feature dependent, but most common features should use a value of 2.
*/
public FeatureLaplaceScaleSpace(
GeneralFeatureDetector<T, D> detector,
ImageFunctionSparse<T> sparseLaplace,
double scalePower) {
this.detector = detector;
this.sparseLaplace = sparseLaplace;
this.baseThreshold = detector.getThreshold();
this.scalePower = scalePower;
localSpace = (T[]) new ImageSingleBand[3];
maximums = new List[3];
maximums[0] = new ArrayList<Point2D_I16>();
maximums[1] = new ArrayList<Point2D_I16>();
maximums[2] = new ArrayList<Point2D_I16>();
}
