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add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm (Operator)

Name

add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm — Add training samples from an image to the training data of a Gaussian Mixture Model.

Signature

add_samples_image_class_gmm(Image, ClassRegions : : GMMHandle, Randomize : )

Herror add_samples_image_class_gmm(const Hobject Image, const Hobject ClassRegions, const Hlong GMMHandle, double Randomize)

Herror T_add_samples_image_class_gmm(const Hobject Image, const Hobject ClassRegions, const Htuple GMMHandle, const Htuple Randomize)

Herror add_samples_image_class_gmm(Hobject Image, Hobject ClassRegions, const HTuple& GMMHandle, const HTuple& Randomize)

void HClassGmm::AddSamplesImageClassGmm(const HImage& Image, const HRegionArray& ClassRegions, const HTuple& Randomize) const

void AddSamplesImageClassGmm(const HObject& Image, const HObject& ClassRegions, const HTuple& GMMHandle, const HTuple& Randomize)

void HImage::AddSamplesImageClassGmm(const HRegion& ClassRegions, const HClassGmm& GMMHandle, double Randomize) const

void HClassGmm::AddSamplesImageClassGmm(const HImage& Image, const HRegion& ClassRegions, double Randomize) const

void HOperatorSetX.AddSamplesImageClassGmm(
[in] IHUntypedObjectX* Image, [in] IHUntypedObjectX* ClassRegions, [in] VARIANT GMMHandle, [in] VARIANT Randomize)

void HImageX.AddSamplesImageClassGmm(
[in] IHRegionX* ClassRegions, [in] IHClassGmmX* GMMHandle, [in] double Randomize)

void HClassGmmX.AddSamplesImageClassGmm(
[in] IHImageX* Image, [in] IHRegionX* ClassRegions, [in] double Randomize)

static void HOperatorSet.AddSamplesImageClassGmm(HObject image, HObject classRegions, HTuple GMMHandle, HTuple randomize)

void HImage.AddSamplesImageClassGmm(HRegion classRegions, HClassGmm GMMHandle, double randomize)

void HClassGmm.AddSamplesImageClassGmm(HImage image, HRegion classRegions, double randomize)

Description

add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm adds training samples from the ImageImageImageImageImageimage to the Gaussian Mixture Model (GMM) given by GMMHandleGMMHandleGMMHandleGMMHandleGMMHandleGMMHandle. add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm is used to store the training samples before a classifier to be used for the pixel classification of multichannel images with classify_image_class_gmmclassify_image_class_gmmClassifyImageClassGmmclassify_image_class_gmmClassifyImageClassGmmClassifyImageClassGmm is trained. add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm works analogously to add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmm. The ImageImageImageImageImageimage must have a number of channels equal to NumDimNumDimNumDimNumDimNumDimnumDim, as specified with create_class_gmmcreate_class_gmmCreateClassGmmcreate_class_gmmCreateClassGmmCreateClassGmm. The training regions for the NumClassesNumClassesNumClassesNumClassesNumClassesnumClasses pixel classes are passed in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions. Hence, ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions must be a tuple containing NumClassesNumClassesNumClassesNumClassesNumClassesnumClasses 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 GMM by calling add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmm 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 a lower classification performance. Image data of integer type can be particularly badly suited for modelling with a GMM. RandomizeRandomizeRandomizeRandomizeRandomizerandomize can be used to overcome this problem, as explained in add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmm.

Parallelization

Parameters

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

Training image.

ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions (input_object)  region-array objectHRegionHRegionHRegionArrayHRegionXHobject

Regions of the classes to be trained.

GMMHandleGMMHandleGMMHandleGMMHandleGMMHandleGMMHandle (input_control)  class_gmm HClassGmm, HTupleHTupleHClassGmm, HTupleHClassGmmX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

GMM handle.

RandomizeRandomizeRandomizeRandomizeRandomizerandomize (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

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

Result

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

Possible Predecessors

create_class_gmmcreate_class_gmmCreateClassGmmcreate_class_gmmCreateClassGmmCreateClassGmm

Possible Successors

train_class_gmmtrain_class_gmmTrainClassGmmtrain_class_gmmTrainClassGmmTrainClassGmm, write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmm

Alternatives

read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmm

See also

classify_image_class_gmmclassify_image_class_gmmClassifyImageClassGmmclassify_image_class_gmmClassifyImageClassGmmClassifyImageClassGmm, add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmm, clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmm, get_sample_num_class_gmmget_sample_num_class_gmmGetSampleNumClassGmmget_sample_num_class_gmmGetSampleNumClassGmmGetSampleNumClassGmm, get_sample_class_gmmget_sample_class_gmmGetSampleClassGmmget_sample_class_gmmGetSampleClassGmmGetSampleClassGmm

Module

Foundation


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