learn_ndim_normT_learn_ndim_normLearnNdimNormLearnNdimNorm (Operator)

Name

learn_ndim_normT_learn_ndim_normLearnNdimNormLearnNdimNorm — Construct classes for class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNorm.

Signature

Description

learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNorm 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_normClassNdimNormClassNdimNormClassNdimNorm. BackgroundBackgroundBackgroundBackgroundbackground determines a class of pixels not to be found in class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNorm. 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 Euclidean 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_normLearnNdimNormLearnNdimNormLearnNdimNorm and learn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBoxLearnNdimBox 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_normLearnNdimNormLearnNdimNormLearnNdimNorm, 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.

Execution Information

• Multithreading type: exclusive (runs in parallel only with independent operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

Result

learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNorm 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_systemSetSystemSetSystemSetSystem. If necessary, an exception is raised.

Possible Predecessors

min_max_graymin_max_grayMinMaxGrayMinMaxGrayMinMaxGray, sobel_ampsobel_ampSobelAmpSobelAmpSobelAmp, binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilter, gauss_filtergauss_filterGaussFilterGaussFilterGaussFilter, reduce_domainreduce_domainReduceDomainReduceDomainReduceDomain, diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussDiffOfGauss

Possible Successors

class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNorm, connectionconnectionConnectionConnectionConnection, dilation1dilation1Dilation1Dilation1Dilation1, erosion1erosion1Erosion1Erosion1Erosion1, openingopeningOpeningOpeningOpening, closingclosingClosingClosingClosing, rank_regionrank_regionRankRegionRankRegionRankRegion, shape_transshape_transShapeTransShapeTransShapeTrans, skeletonskeletonSkeletonSkeletonSkeleton

Alternatives

learn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBoxLearnNdimBox, learn_class_boxlearn_class_boxLearnClassBoxLearnClassBoxLearnClassBox

See also

class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNorm, histo_2dimhisto_2dimHisto2dimHisto2dimHisto2dim

References

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

Module

Foundation