learn_ndim_normT_learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm (Operator)

Name

learn_ndim_normT_learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm — Construct classes for class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm.

Signature

Description

learn_ndim_normlearn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm generates classification clusters from the region ForegroundForegroundForegroundForegroundforeground and the corresponding gray values in the multi-channel image ImageImageImageImageimage, which can be used in class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm. BackgroundBackgroundBackgroundBackgroundbackground determines a class of pixels not to be found in class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm. This parameter may be empty (empty object).

The parameter DistanceDistanceDistanceDistancedistance determines the maximum distance RadiusRadiusRadiusRadiusradius of the clusters. It describes the minimum distance between two cluster centers. If the parameter DistanceDistanceDistanceDistancedistance is small the (small) hyper-cubes or hyper-spheres can approximate the feature space well. Simultaneously the runtime during classification increases.

The ratio of the number of pixels in a cluster to the total number of pixels (in percent) must be larger than the value of MinNumberPercentMinNumberPercentMinNumberPercentMinNumberPercentminNumberPercent, otherwise the cluster is not returned. MinNumberPercentMinNumberPercentMinNumberPercentMinNumberPercentminNumberPercent serves to eliminate outliers in the training set. If it is chosen too large many clusters are suppressed.

Two different clustering procedures can be selected: The minimum distance algorithm (n-dimensional hyper-spheres) and the maximum algorithm (n-dimensional hyper-cubes) for describing the pixels of the image to classify in the n-dimensional histogram (parameter MetricMetricMetricMetricmetric). The Euclidian metric usually yields the better results, but takes longer to compute. The parameter QualityQualityQualityQualityquality returns the quality of the clustering. It is a measure of overlap between the rejection class and the classificator classes. Values larger than 0 denote the corresponding ratio of overlap. If no rejection region is given, its value is set to 1. The regions in BackgroundBackgroundBackgroundBackgroundbackground do not influence on the clustering. They are merely used to check the results that can be expected.

From a user's point of view the key difference between learn_ndim_normlearn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm and learn_ndim_boxlearn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBox is that in the latter case the rejection class affects the classification process itself. Here, a hyper plane is generated that separates ForegroundForegroundForegroundForegroundforeground and BackgroundBackgroundBackgroundBackgroundbackground classes, so that no points in feature space are classified incorrectly. As for learn_ndim_normlearn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm, however, an overlap between ForegroundForegroundForegroundForegroundforeground and BackgroundBackgroundBackgroundBackgroundbackground class is allowed. This has its effect on the return value QualityQualityQualityQualityquality. The larger the overlap, the smaller this value.

Parallelization

• Multithreading type: exclusive (runs in parallel only with independent operators).
• Multithreading scope: local (may only be called from the same thread in which the window, model, or tool instance was created).
• Processed without parallelization.

Parameters

Result

learn_ndim_normlearn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNorm returns 2 (H_MSG_TRUE) if all parameters are correct. The behavior with respect to the input images can be determined by setting the values of the flags 'no_object_result'"no_object_result""no_object_result""no_object_result""no_object_result" and 'empty_region_result'"empty_region_result""empty_region_result""empty_region_result""empty_region_result" with set_systemset_systemset_systemSetSystemSetSystem. If necessary, an exception is raised.

Possible Predecessors

min_max_graymin_max_graymin_max_grayMinMaxGrayMinMaxGray, sobel_ampsobel_ampsobel_ampSobelAmpSobelAmp, binomial_filterbinomial_filterbinomial_filterBinomialFilterBinomialFilter, gauss_imagegauss_imagegauss_imageGaussImageGaussImage, reduce_domainreduce_domainreduce_domainReduceDomainReduceDomain, diff_of_gaussdiff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGauss

Possible Successors

class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm, connectionconnectionconnectionConnectionConnection, dilation1dilation1dilation1Dilation1Dilation1, erosion1erosion1erosion1Erosion1Erosion1, openingopeningopeningOpeningOpening, closingclosingclosingClosingClosing, rank_regionrank_regionrank_regionRankRegionRankRegion, shape_transshape_transshape_transShapeTransShapeTrans, skeletonskeletonskeletonSkeletonSkeleton

Alternatives

learn_ndim_boxlearn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBox, learn_class_boxlearn_class_boxlearn_class_boxLearnClassBoxLearnClassBox

See also

class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm, class_ndim_boxclass_ndim_boxclass_ndim_boxClassNdimBoxClassNdimBox, histo_2dimhisto_2dimhisto_2dimHisto2dimHisto2dim

References

P. Haberäcker, “Digitale Bildverarbeitung”; Hanser-Studienbücher, München, Wien, 1987

Module

Foundation