Add training samples from an image to the training data of a Gaussian Mixture Model.
add_samples_image_class_gmm adds training samples from the Image to the Gaussian Mixture Model (GMM) given by GMMHandle. add_samples_image_class_gmm is used to store the training samples before a classifier to be used for the pixel classification of multichannel images with classify_image_class_gmm is trained. add_samples_image_class_gmm works analogously to add_sample_class_gmm. The Image must have a number of channels equal to NumDim, as specified with create_class_gmm. The training regions for the NumClasses pixel classes are passed in ClassRegions. Hence, ClassRegions must be a tuple containing NumClasses regions. The order of the regions in ClassRegions determines the class of the pixels. If there are no samples for a particular class in Image an 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 GMM by calling add_samples_image_class_gmm multiple times with the different images and suitably chosen regions. The regions in ClassRegions should contain representative training samples for the respective classes. Hence, they need not cover the entire image. The regions in ClassRegions 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 a lower classification performance. Image data of integer type can be particularly badly suited for modelling with a GMM. Randomize can be used to overcome this problem, as explained in add_sample_class_gmm.
|
Image (input_object) |
(multichannel-)image -> object : byte / cyclic / direction / int1 / int2 / uint2 / int4 / real |
| Training image. | |
|
ClassRegions (input_object) |
region-array -> object |
| Regions of the classes to be trained. | |
|
GMMHandle (input_control) |
class_gmm -> integer |
| GMM handle. | |
|
Randomize (input_control) |
real -> real |
| Standard deviation of the Gaussian noise added to the training data. | |
| Default value: 0.0 | |
| Suggested values: 0.0, 1.5, 2.0 | |
| Restriction: Randomize >= 0.0 | |
If the parameters are valid, the operator add_samples_image_class_gmm returns the value 2 (H_MSG_TRUE). If necessary an exception handling is raised.
add_samples_image_class_gmm is processed completely exclusively without parallelization.
train_class_gmm, write_samples_class_gmm
classify_image_class_gmm, add_sample_class_gmm, clear_samples_class_gmm, get_sample_num_class_gmm, get_sample_class_gmm
Foundation