classify_image_class_gmm — Classify an image with a Gaussian Mixture Model.
classify_image_class_gmm performs a pixel classification with the Gaussian Mixture Model (GMM) GMMHandle on the multichannel image Image. Before calling classify_image_class_gmm the GMM must be trained with train_class_gmm. Image must have NumDim channels, as specified with create_class_gmm. On output, ClassRegions contains NumClasses regions as the result of the classification. The parameter RejectionThreshold can be used to reject pixels that have an uncertain classification. RejectionThreshold represents a threshold on the K-sigma probability measure returned by the classification (see classify_class_gmm and evaluate_class_gmm). All pixels having a probability below RejectionThreshold are not assigned to any class.
Threshold for the rejection of the classification.
Default value: 0.5
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: RejectionThreshold >= 0.0 && RejectionThreshold <= 1.0
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_gmm (3, 4, [1,30], 'full', 'none',0, 42, GMMHandle) add_samples_image_class_gmm (Image, Classes, GMMHandle, 1.5) get_sample_num_class_gmm (GMMHandle, NumSamples) train_class_gmm (GMMHandle, 150, 1e-4, 'training', 1e-4, Centers, Iter) classify_image_class_gmm (Image, ClassRegions, GMMHandle, 0.0001) clear_class_gmm (GMMHandle)
If the parameters are valid, the operator classify_image_class_gmm returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
classify_image_class_knn, classify_image_class_mlp, classify_image_class_svm, classify_image_class_lut, class_ndim_box, class_ndim_norm, class_2dim_sup