Learner object that creates a classifier from an image list. More...
Public Member Functions | |
ClassifierFactory () | |
Construct a learner that has no training set set yet. | |
Classifier | Learn (TrainingSet trainingSet) |
Learn a new classifier from the trainingSet using the parameters stored in the properties of this object. More... | |
Static Public Attributes | |
const int | IndifferenceRadiusDefault = 6 |
Default value for the IndifferenceRadius property. | |
const double | NegativesDensityDefault = 1.0 |
Default value for the NegativesDensity property. | |
const int | ContrastTriggerDefault = 8 |
Default value for the ContrastTrigger property. | |
const int | MinPairFeaturesDefault = 20 |
Default value for the MinPairFeatures property. | |
Properties | |
int | IndifferenceRadius [get, set] |
Minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image. More... | |
double | NegativesDensity [get, set] |
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. More... | |
int | ContrastTrigger [get, set] |
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. | |
int | MinPairFeatures [get, set] |
Minimum number of features to extract for each model when building a classifier. | |
Events | |
EventHandler< LearnProgressEventArgs > | LearnProgress |
Event that will inform about the progress of an ongoing learn operation. | |
Learner object that creates a classifier from an image list.
Classifier Learn | ( | TrainingSet | trainingSet | ) |
Learn a new classifier from the trainingSet using the parameters stored in the properties of this object.
trainingSet | training set from which to learn |
ArgumentNullException | when trying to pass a null reference as the trainingSet parameter |
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getset |
Minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image.
ArgumentOutOfRangeException | when trying to set an indifference radius less than 0 |
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getset |
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.
ArgumentOutOfRangeException | when trying to set an invalid scan density (valid values are within the range [0...1] |