Name
add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn — Add training samples from an image to the training data of a
k-Nearest-Neighbor classifier.
add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn adds training samples from the
ImageImageImageImageImageimage to the k-Nearest-Neighbor (k-NN) given by
KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleKNNHandle. add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn is used to
store the training samples before a classifier is used for the
pixel classification of multichannel images with
classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn.
add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn works analogously to
add_sample_class_knnadd_sample_class_knnAddSampleClassKnnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnn. The ImageImageImageImageImageimage must have a number
of channels equal to NumDimNumDimNumDimNumDimNumDimnumDim, as specified with
create_class_knncreate_class_knnCreateClassKnncreate_class_knnCreateClassKnnCreateClassKnn. ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions must be a tuple
containing of at least 2 regions. The order of the regions
in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions determines the class of the pixels. If
there are no samples for a particular class in ImageImageImageImageImageimage an
empty region must be passed at the position of the class in
ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions. 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 add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn
multiple times with different images and suitably chosen regions. The
regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should contain representative
training samples for the respective classes. Hence, they do not need
to cover the entire image. The regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should
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
classification performance.
- Multithreading type: exclusive (runs in parallel only with independent operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Regions of the classes to be trained.
Handle of the k-NN classifier.
If the parameters are valid, the operator
add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
create_class_knncreate_class_knnCreateClassKnncreate_class_knnCreateClassKnnCreateClassKnn
train_class_knntrain_class_knnTrainClassKnntrain_class_knnTrainClassKnnTrainClassKnn
add_sample_class_knnadd_sample_class_knnAddSampleClassKnnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnn
classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn,
add_sample_class_knnadd_sample_class_knnAddSampleClassKnnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnn,
add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm
Foundation