Name
add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp — Add training samples from an image to the training data of a
multilayer perceptron.
add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp adds training samples from the
image ImageImageImageImageImageimage to the multilayer perceptron (MLP) given by
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle. add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp is used to
store the training samples before a classifier to be used for the
pixel classification of multichannel images with
classify_image_class_mlpclassify_image_class_mlpClassifyImageClassMlpclassify_image_class_mlpClassifyImageClassMlpClassifyImageClassMlp is trained.
add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp works analogously to
add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp. Because here the MLP is always used
for classification, OutputFunction = 'softmax'"softmax""softmax""softmax""softmax""softmax"
must be specified when the MLP is created with
create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp. The image ImageImageImageImageImageimage must have a
number of channels equal to NumInput, as specified with
create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp. The training regions for the
NumOutput pixel classes are passed in
ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions. Hence, ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions must be a tuple
containing NumOutput 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 MLP by calling add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp multiple
times with the different images and suitably chosen regions. The
regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should contain representative
training samples for the respective classes. Hence, they need not
cover the entire image. The regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should
not overlap each other, because this would lead to the fact that in
the training data the samples from the overlapping areas would be
assigned to multiple classes, which may lead to slower convergence
of the training and a lower classification performance.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.
Regions of the classes to be trained.
If the parameters are valid, the operator
add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp
train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp,
write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlp
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp
classify_image_class_mlpclassify_image_class_mlpClassifyImageClassMlpclassify_image_class_mlpClassifyImageClassMlpClassifyImageClassMlp,
add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp,
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp,
get_sample_num_class_mlpget_sample_num_class_mlpGetSampleNumClassMlpget_sample_num_class_mlpGetSampleNumClassMlpGetSampleNumClassMlp,
get_sample_class_mlpget_sample_class_mlpGetSampleClassMlpget_sample_class_mlpGetSampleClassMlpGetSampleClassMlp,
add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm
Foundation