learn_ndim_normT_learn_ndim_normLearnNdimNormLearnNdimNormlearn_ndim_norm (Operator)
Name
learn_ndim_normT_learn_ndim_normLearnNdimNormLearnNdimNormlearn_ndim_norm
— Construct classes for class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm
.
Signature
void LearnNdimNorm(const HObject& Foreground, const HObject& Background, const HObject& Image, const HTuple& Metric, const HTuple& Distance, const HTuple& MinNumberPercent, HTuple* Radius, HTuple* Center, HTuple* Quality)
HTuple HImage::LearnNdimNorm(const HRegion& Foreground, const HRegion& Background, const HString& Metric, const HTuple& Distance, const HTuple& MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HImage::LearnNdimNorm(const HRegion& Foreground, const HRegion& Background, const HString& Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HImage::LearnNdimNorm(const HRegion& Foreground, const HRegion& Background, const char* Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HImage::LearnNdimNorm(const HRegion& Foreground, const HRegion& Background, const wchar_t* Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
(Windows only)
HTuple HRegion::LearnNdimNorm(const HRegion& Background, const HImage& Image, const HString& Metric, const HTuple& Distance, const HTuple& MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HRegion::LearnNdimNorm(const HRegion& Background, const HImage& Image, const HString& Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HRegion::LearnNdimNorm(const HRegion& Background, const HImage& Image, const char* Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
HTuple HRegion::LearnNdimNorm(const HRegion& Background, const HImage& Image, const wchar_t* Metric, double Distance, double MinNumberPercent, HTuple* Center, double* Quality) const
(Windows only)
static void HOperatorSet.LearnNdimNorm(HObject foreground, HObject background, HObject image, HTuple metric, HTuple distance, HTuple minNumberPercent, out HTuple radius, out HTuple center, out HTuple quality)
HTuple HImage.LearnNdimNorm(HRegion foreground, HRegion background, string metric, HTuple distance, HTuple minNumberPercent, out HTuple center, out double quality)
HTuple HImage.LearnNdimNorm(HRegion foreground, HRegion background, string metric, double distance, double minNumberPercent, out HTuple center, out double quality)
HTuple HRegion.LearnNdimNorm(HRegion background, HImage image, string metric, HTuple distance, HTuple minNumberPercent, out HTuple center, out double quality)
HTuple HRegion.LearnNdimNorm(HRegion background, HImage image, string metric, double distance, double minNumberPercent, out HTuple center, out double quality)
Description
learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm
generates classification clusters from the
region ForegroundForegroundForegroundForegroundforegroundforeground
and the corresponding gray values in
the multi-channel image ImageImageImageImageimageimage
, which can be used in
class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm
. BackgroundBackgroundBackgroundBackgroundbackgroundbackground
determines a class
of pixels not to be found in class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm
. This
parameter may be empty (empty object).
The parameter DistanceDistanceDistanceDistancedistancedistance
determines the maximum distance
RadiusRadiusRadiusRadiusradiusradius
of the clusters. It describes the minimum distance
between two cluster centers. If the parameter DistanceDistanceDistanceDistancedistancedistance
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
MinNumberPercentMinNumberPercentMinNumberPercentMinNumberPercentminNumberPercentmin_number_percent
, otherwise the cluster is not returned.
MinNumberPercentMinNumberPercentMinNumberPercentMinNumberPercentminNumberPercentmin_number_percent
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
MetricMetricMetricMetricmetricmetric
). The Euclidian metric usually yields the better
results, but takes longer to compute. The parameter
QualityQualityQualityQualityqualityquality
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 BackgroundBackgroundBackgroundBackgroundbackgroundbackground
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_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm
and learn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBoxLearnNdimBoxlearn_ndim_box
is that in the
latter case the rejection class affects the classification process
itself. Here, a hyper plane is generated that separates
ForegroundForegroundForegroundForegroundforegroundforeground
and BackgroundBackgroundBackgroundBackgroundbackgroundbackground
classes, so that no
points in feature space are classified incorrectly.
As for learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm
, however, an overlap between
ForegroundForegroundForegroundForegroundforegroundforeground
and BackgroundBackgroundBackgroundBackgroundbackgroundbackground
class is allowed.
This has its effect on the return value QualityQualityQualityQualityqualityquality
.
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
ForegroundForegroundForegroundForegroundforegroundforeground
(input_object) region(-array) →
objectHRegionHObjectHRegionHobject
Foreground pixels to be trained.
BackgroundBackgroundBackgroundBackgroundbackgroundbackground
(input_object) region(-array) →
objectHRegionHObjectHRegionHobject
Background pixels to be trained (rejection class).
ImageImageImageImageimageimage
(input_object) (multichannel-)image(-array) →
objectHImageHObjectHImageHobject (byte)
Multi-channel training image.
MetricMetricMetricMetricmetricmetric
(input_control) string →
HTuplestrHTupleHtuple (string) (string) (HString) (char*)
Metric to be used.
Default value:
'euclid'
"euclid"
"euclid"
"euclid"
"euclid"
"euclid"
List of values: 'euclid'"euclid""euclid""euclid""euclid""euclid", 'maximum'"maximum""maximum""maximum""maximum""maximum"
DistanceDistanceDistanceDistancedistancedistance
(input_control) number →
HTupleUnion[int, float]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)
Maximum cluster radius.
Default value: 10.0
Suggested values: 1.0, 2.0, 3.0, 4.0, 6.0, 8.0, 10.0, 13.0, 17.0, 24.0, 30.0, 40.0
Typical range of values: 1.0
≤
Distance
Distance
Distance
Distance
distance
distance
(lin)
Minimum increment: 0.01
Recommended increment: 1.0
MinNumberPercentMinNumberPercentMinNumberPercentMinNumberPercentminNumberPercentmin_number_percent
(input_control) number →
HTupleUnion[int, float]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)
The ratio of the number of pixels in a cluster to the
total number of pixels (in percent) must be larger than
MinNumberPercent (otherwise the cluster is not output).
Default value: 0.01
Suggested values: 0.001, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0
Typical range of values: 0.0
≤
MinNumberPercent
MinNumberPercent
MinNumberPercent
MinNumberPercent
minNumberPercent
min_number_percent
≤
100.0
(lin)
Minimum increment: 0.01
Recommended increment: 0.1
RadiusRadiusRadiusRadiusradiusradius
(output_control) real-array →
HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Cluster radii or half edge lengths.
CenterCenterCenterCentercentercenter
(output_control) real-array →
HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Coordinates of all cluster centers.
QualityQualityQualityQualityqualityquality
(output_control) real →
HTuplefloatHTupleHtuple (real) (double) (double) (double)
Overlap of the rejection class with the classified
objects (1: no overlap).
Assertion: 0 <= Quality && Quality <= 1
Result
learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm
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""no_object_result" and 'empty_region_result'"empty_region_result""empty_region_result""empty_region_result""empty_region_result""empty_region_result"
with set_systemset_systemSetSystemSetSystemSetSystemset_system
. If necessary, an exception is raised.
Possible Predecessors
min_max_graymin_max_grayMinMaxGrayMinMaxGrayMinMaxGraymin_max_gray
,
sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp
,
binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter
,
gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter
,
reduce_domainreduce_domainReduceDomainReduceDomainReduceDomainreduce_domain
,
diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussDiffOfGaussdiff_of_gauss
Possible Successors
class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm
,
connectionconnectionConnectionConnectionConnectionconnection
,
dilation1dilation1Dilation1Dilation1Dilation1dilation1
,
erosion1erosion1Erosion1Erosion1Erosion1erosion1
,
openingopeningOpeningOpeningOpeningopening
,
closingclosingClosingClosingClosingclosing
,
rank_regionrank_regionRankRegionRankRegionRankRegionrank_region
,
shape_transshape_transShapeTransShapeTransShapeTransshape_trans
,
skeletonskeletonSkeletonSkeletonSkeletonskeleton
Alternatives
learn_ndim_boxlearn_ndim_boxLearnNdimBoxLearnNdimBoxLearnNdimBoxlearn_ndim_box
,
learn_class_boxlearn_class_boxLearnClassBoxLearnClassBoxLearnClassBoxlearn_class_box
See also
class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm
,
histo_2dimhisto_2dimHisto2dimHisto2dimHisto2dimhisto_2dim
References
P. Haberäcker, “Digitale Bildverarbeitung”;
Hanser-Studienbücher, München, Wien, 1987
Module
Foundation