classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn (Operator)

Name

classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn — Classify an image with a k-Nearest-Neighbor classifier.

Signature

classify_image_class_knn(Image : ClassRegions, DistanceImage : KNNHandle, RejectionThreshold : )

Description

classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn performs a pixel classification with a k-Nearest-Neighbor classifier (k-NN) KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleKNNHandle on the multichannel image ImageImageImageImageImageimage. Before calling classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn the k-NN classifier must be trained with train_class_knntrain_class_knnTrainClassKnntrain_class_knnTrainClassKnnTrainClassKnn. ImageImageImageImageImageimage must have NumDimNumDimNumDimNumDimNumDimnumDim channels, as specified with create_class_knncreate_class_knnCreateClassKnncreate_class_knnCreateClassKnnCreateClassKnn. On output, ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions contains NumClassesNumClassesNumClassesNumClassesNumClassesnumClasses regions as the result of the classification. Note that the order of the regions that are returned in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions corresponds to the order of the classes as defined by the training regions in add_samples_image_class_knnadd_samples_image_class_knnAddSamplesImageClassKnnadd_samples_image_class_knnAddSamplesImageClassKnnAddSamplesImageClassKnn. The parameter RejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdrejectionThreshold can be used to reject pixels that have an uncertain classification. RejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdrejectionThreshold represents a threshold on the distance to the nearest neighbor returned by the classification. All pixels having a probability below RejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdrejectionThreshold are not assigned to any class. DistanceImageDistanceImageDistanceImageDistanceImageDistanceImagedistanceImage contains the distance of each pixel to its nearest neighbor.

Parallelization

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on internal data level.

Parameters

ImageImageImageImageImageimage (input_object) (multichannel-)image → object (byte / cyclic / direction / int1 / int2 / uint2 / int4 / real)

Input image.

ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions (output_object) region-array → object (real)

Segmented classes.

DistanceImageDistanceImageDistanceImageDistanceImageDistanceImagedistanceImage (output_object) image → object

Distance of the pixel's nearest neighbor.

KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleKNNHandle (input_control) class_knn → (integer)

Handle of the k-NN classifier.

RejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdRejectionThresholdrejectionThreshold (input_control) real → (real)

Threshold for the rejection of the classification.

Default value: 0.5

Suggested values: 0.0, 0.1, 0.2, 0.3, 5.0, 10.0, 255.0

Restriction: RejectionThreshold >= 0.0

Example (HDevelop)

read_image (Image, 'ic')
gen_rectangle1 (Board, 80, 320, 110, 350)
gen_rectangle1 (Capacitor, 359, 263, 371, 302)
gen_rectangle1 (Resistor, 200, 252, 290, 256)
gen_rectangle1 (IC, 180, 135, 216, 165)
concat_obj (Board, Capacitor, Classes)
concat_obj (Classes, Resistor, Classes)
concat_obj (Classes, IC, Classes)
create_class_knn (3, KNNHandle)
add_samples_image_class_knn (Image, Classes, KNNHandle)
get_sample_num_class_knn (KNNHandle, NumSamples)
train_class_knn (KNNHandle, [], [])
classify_image_class_knn (Image, ClassRegions, DistanceImage, KNNHandle, 0.5)
dev_display (ClassRegions)
clear_class_knn (KNNHandle)

If the parameters are valid, the operator classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnn returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.