ClassifierFactory Properties

Minimum contrast a Minos feature must achieve before it is eligible to become part of the classifier. Lower values will lead to a classifier that is more sensitive in low-contrast situation but might also increase the number of false positive results that need to be filtered out e.g. by means of their quality measure.

Minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image.

Minimum number of features to extract for each model when building a classifier.

Scan density with which to extract counter samples from the training set images. Higher densities will lead to a classifier that is potentially more robust versus false positive detections, but will also result in increased learning times.