add_samples_image_class_knn — Add training samples from an image to the training data of a
add_samples_image_class_knn adds training samples from the
Image to the k-Nearest-Neighbor (k-NN) given by
add_samples_image_class_knn is used to
store the training samples before a classifier is used for the
pixel classification of multichannel images with
add_samples_image_class_knn works analogously to
Image must have a number
of channels equal to
NumDim, as specified with
ClassRegions must be a tuple
containing of at least 2 regions. The order of the regions
ClassRegions determines the class of the pixels. If
there are no samples for a particular class in
empty region must be passed at the position of the class in
ClassRegions. With this mechanism it is possible to use
multiple images to add training samples for all relevant classes to
the k-NN classifier by calling
multiple times with different images and suitably chosen regions. The
ClassRegions should contain representative
training samples for the respective classes. Hence, they do not need
to cover the entire image. The regions in
not overlap each other, as these samples from overlapping areas would be
assigned to multiple classes in the training data, which may lead to a lower
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
→object (byte / cyclic / direction / int1 / int2 / uint2 / int4 / real)
Regions of the classes to be trained.
KNNHandle(input_control, state is modified) class_knn
Handle of the k-NN classifier.
If the parameters are valid, the operator
add_samples_image_class_knn returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.