ClassifierFactory Class

Construct a learner that has no training set set yet.

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.

Determines whether the specified Object is equal to the current Object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Learn a new classifier from the trainingSet using the parameters stored in the properties of this object.

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Event that will inform about the progress of an ongoing learn operation.

Default value for the ContrastTrigger property.

Default value for the IndifferenceRadius property.

Default value for the MinPairFeatures property.

Default value for the NegativesDensity property.