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vigra::acc Namespace Reference | ![]() |
Classes | |
| class | AbsPowerSum |
Basic statistic. AbsPowerSum<N> = . More... | |
| class | AccumulatorChain |
| Create an accumulator chain containing the selected statistics and their dependencies. More... | |
| class | AccumulatorChainArray |
| Create an array of accumulator chains containing the selected per-region and global statistics and their dependencies. More... | |
| class | ArgMaxWeight |
| Basic statistic. Data where weight assumes its maximal value. More... | |
| class | ArgMinWeight |
| Basic statistic. Data value where weight assumes its minimal value. More... | |
| class | AutoRangeHistogram |
| Histogram where range mapping bounds are defined by minimum and maximum of data. More... | |
| class | Central |
| Modifier. Substract mean before computing statistic. More... | |
| class | Central< PowerSum< 2 > > |
| Spezialization: works in pass 1, operator+=() supported (merging supported). More... | |
| class | Central< PowerSum< 3 > > |
| Specialization: works in pass 2, operator+=() supported (merging supported). More... | |
| class | Central< PowerSum< 4 > > |
| Specialization: works in pass 2, operator+=() supported (merging supported). More... | |
| class | Coord |
| Modifier. Compute statistic from pixel coordinates rather than from pixel values. More... | |
| class | CoordinateSystem |
| Basic statistic. Identity matrix of appropriate size. More... | |
| class | DataArg |
| Specifies index of data in CoupledHandle. More... | |
| class | DivideByCount |
| Modifier. Divide statistic by Count: DivideByCount<TAG> = TAG / Count . More... | |
| class | DivideUnbiased |
| Modifier. Divide statistics by Count-1: DivideUnbiased<TAG> = TAG / (Count-1) More... | |
| class | DynamicAccumulatorChain |
| Create a dynamic accumulator chain containing the selected statistics and their dependencies. More... | |
| class | DynamicAccumulatorChainArray |
| Create an array of dynamic accumulator chains containing the selected per-region and global statistics and their dependencies. More... | |
| class | FlatScatterMatrix |
| Basic statistic. Flattened uppter-triangular part of scatter matrix. More... | |
| class | Global |
| Modifier. Compute statistic globally rather than per region. More... | |
| class | GlobalRangeHistogram |
| Like AutoRangeHistogram, but use global min/max rather than region min/max. More... | |
| class | IntegerHistogram |
| Histogram where data values are equal to bin indices. More... | |
| class | Kurtosis |
| Basic statistic. Kurtosis. More... | |
| class | LabelArg |
| Specifies index of labels in CoupledHandle. More... | |
| class | Maximum |
| Basic statistic. Maximum value. More... | |
| class | Minimum |
| Basic statistic. Minimum value. More... | |
| class | PowerSum |
Basic statistic. PowerSum<N> = . More... | |
| class | Principal |
| Modifier. Project onto PCA eigenvectors. More... | |
| class | Principal< CoordinateSystem > |
| Specialization (covariance eigenvectors): works in pass 1, operator+=() supported (merging). More... | |
| class | Principal< PowerSum< 2 > > |
| Specialization (covariance eigenvalues): works in pass 1, operator+=() supported (merging). More... | |
| class | RootDivideByCount |
| Modifier. RootDivideByCount<TAG> = sqrt( TAG/Count ) More... | |
| class | RootDivideUnbiased |
| Modifier. RootDivideUnbiased<TAG> = sqrt( TAG / (Count-1) ) More... | |
| class | ScatterMatrixEigensystem |
| struct | Select |
| Wrapper for MakeTypeList that additionally performs tag standardization. More... | |
| class | Skewness |
| Basic statistic. Skewness. More... | |
| class | StandardQuantiles |
| Compute (0%, 10%, 25%, 50%, 75%, 90%, 100%) quantiles from given histogram. More... | |
| class | UnbiasedKurtosis |
| Basic statistic. Unbiased Kurtosis. More... | |
| class | UnbiasedSkewness |
| Basic statistic. Unbiased Skewness. More... | |
| class | UserRangeHistogram |
| Histogram where user provides bounds for linear range mapping from values to indices. More... | |
| class | WeightArg |
| Specifies index of data in CoupledHandle. More... | |
| class | Weighted |
| Compute weighted version of the statistic. More... | |
Typedefs | |
| typedef AbsPowerSum< 1 > | AbsSum |
| Alias. Absolute sum. | |
| typedef Weighted< RegionAxes > | AxesOfInertia |
| Alias. Axes of inertia. | |
| typedef Weighted< RegionCenter > | CenterOfMass |
| Alias. Center of mass. | |
| typedef PowerSum< 0 > | Count |
| Alias. Count. | |
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typedef DivideByCount < FlatScatterMatrix > | Covariance |
| Alias. Covariance. | |
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typedef DivideByCount < ScatterMatrixEigensystem > | CovarianceEigensystem |
| Alias. Covariance eigensystem. | |
| typedef DivideByCount< Sum > | Mean |
| Alias. Mean. | |
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typedef DivideByCount < SumOfAbsDifferences > | MeanAbsoluteDeviation |
| Alias. Mean absolute deviation. | |
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typedef Weighted< Coord < Principal< Variance > > > | MomentsOfInertia |
| Alias. Moments of inertia. | |
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typedef Coord< Principal < CoordinateSystem > > | RegionAxes |
| Alias. Region axes. | |
| typedef Coord< Mean > | RegionCenter |
| Alias. Region center. | |
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typedef Coord< Principal < StdDev > > | RegionRadii |
| Alias. Region radii. | |
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typedef RootDivideByCount < SumOfSquares > | RootMeanSquares |
| Alias. Root mean square. | |
| typedef SumOfSquaredDifferences | SSD |
| Alias. Sum of squared differences. | |
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typedef RootDivideByCount < Central< PowerSum< 2 > > > | StdDev |
| Alias. Standard deviation. | |
| typedef PowerSum< 1 > | Sum |
| Alias. Sum. | |
| typedef Central< AbsSum > | SumOfAbsDifferences |
| Alias. Sum of absolute differences. | |
| typedef Central< PowerSum< 2 > > | SumOfSquaredDifferences |
| Alias. Sum of squared differences. | |
| typedef PowerSum< 2 > | SumOfSquares |
| Alias. Sum of squares. | |
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typedef DivideUnbiased < FlatScatterMatrix > | UnbiasedCovariance |
| Alias. Unbiased covariance. | |
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typedef RootDivideUnbiased < Central< PowerSum< 2 > > > | UnbiasedStdDev |
| Alias. Unbiased standard deviation. | |
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typedef DivideUnbiased < Central< PowerSum< 2 > > > | UnbiasedVariance |
| Alias. Unbiased variance. | |
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typedef DivideByCount< Central < PowerSum< 2 > > > | Variance |
| Alias. Variance. | |
Functions | |
| template<class Tag , class A > | |
| void | activate (A &a) |
| template<class ITERATOR , class ACCUMULATOR > | |
| void | extractFeatures (ITERATOR start, ITERATOR end, ACCUMULATOR &a) |
| template<class TAG , class A > | |
| LookupTag< TAG, A >::result_type | get (A const &a) |
| template<class TAG , class A > | |
| LookupTag< TAG, A >::result_type | get (A const &a, MultiArrayIndex label) |
| template<class TAG , class A > | |
| LookupTag< TAG, A >::reference | getAccumulator (A &a) |
| template<class TAG , class A > | |
| LookupTag< TAG, A >::reference | getAccumulator (A &a, MultiArrayIndex label) |
| template<class Tag , class A > | |
| bool | isActive (A const &a) |
This namespace contains the accumulator classes, fundamental statistics and modifiers. See Feature Accumulators for examples of usage.
| LookupTag< TAG, A >::reference getAccumulator | ( | A & | a | ) |
Get a reference to the accumulator 'TAG' in the accumulator chain 'a'. This can be useful for example to update a certain accumulator with data, set individual options or get information about a certain accumulator.
Example of use (set options):
vigra::MultiArray<2, double> data(...); typedef UserRangeHistogram<40> SomeHistogram; //binCount set at compile time typedef UserRangeHistogram<0> SomeHistogram2; // binCount must be set at run-time AccumulatorChain<DataType, Select<SomeHistogram, SomeHistogram2> > a; getAccumulator<SomeHistogram>(a).setMinMax(0.1, 0.9); getAccumulator<SomeHistogram2>(a).setMinMax(0.0, 1.0); extractFeatures(data.begin(), data.end(), a);
Example of use (get information):
vigra::MultiArray<2, double> data(...)); AccumulatorChain<double, Select<Mean, Skewness> > a; std::cout << "passes required for all statistics: " << a.passesRequired() << std::endl; //skewness needs two passes std::cout << "passes required by Mean: " << getAccumulator<Mean>(a).passesRequired() << std::endl;
See Feature Accumulators for more information about feature computation via accumulators.
| LookupTag<TAG, A>::reference vigra::acc::getAccumulator | ( | A & | a, |
| MultiArrayIndex | label | ||
| ) |
Get a reference to the accumulator 'TAG' for region 'label' in the accumulator chain 'a'.
| LookupTag<TAG, A>::result_type vigra::acc::get | ( | A const & | a | ) |
Get the result of the accumulator 'TAG' in the accumulator chain 'a'.
Example of use:
vigra::MultiArray<2, double> data(...); AccumulatorChain<DataType, Select<Variance, Mean, StdDev> > a; extractFeatures(data.begin(), data.end(), a); double mean = get<Mean>(a);
See Feature Accumulators for more information about feature computation via accumulators.
| LookupTag<TAG, A>::result_type vigra::acc::get | ( | A const & | a, |
| MultiArrayIndex | label | ||
| ) |
Get the result of the accumulator 'TAG' for region 'label' in the accumulator chain 'a'.
Example of use:
vigra::MultiArray<2, double> data(...); vigra::MultiArray<2, int> labels(...); typedef vigra::CoupledIteratorType<2, double, int>::type Iterator; typedef Iterator::value_type Handle; AccumulatorChainArray<Handle, Select<DataArg<1>, LabelArg<2>, Mean, Variance> > a; Iterator start = createCoupledIterator(data, labels); Iterator end = start.getEndIterator(); extractFeatures(start,end,a); double mean_of_region_1 = get<Mean>(a,1); double mean_of_background = get<Mean>(a,0);
See Feature Accumulators for more information about feature computation via accumulators.
| void vigra::acc::activate | ( | A & | a | ) |
Activate the dynamic accumulator 'Tag' in the dynamic accumulator chain 'a'. Same as a.activate<Tag>() (see DynamicAccumulatorChain::activate<Tag>() or DynamicAccumulatorChainArray::activate<Tag>()). For run-time activation use DynamicAccumulatorChain::activate(std::string tag) or DynamicAccumulatorChainArray::activate(std::string tag) instead.
See Feature Accumulators for more information about feature computation via accumulators.
| bool vigra::acc::isActive | ( | A const & | a | ) |
Check if the dynamic accumulator 'Tag' in the accumulator chain 'a' is active. Same as a.isActive<Tag>() (see DynamicAccumulatorChain::isActive<Tag>() or DynamicAccumulatorChainArray::isActive<Tag>()). At run-time, use DynamicAccumulatorChain::isActive(std::string tag) const or DynamicAccumulatorChainArray::isActive(std::string tag) const instead.
See Feature Accumulators for more information about feature computation via accumulators.
| void vigra::acc::extractFeatures | ( | ITERATOR | start, |
| ITERATOR | end, | ||
| ACCUMULATOR & | a | ||
| ) |
Generic loop to collect the statistics of the accumulator chain 'a' in as many passes over the data as necessary.
Example of use:
vigra::MultiArray<3, double> data(...); vigra::MultiArray<3, int> labels(...); typedef vigra::CoupledIteratorType<3, double, int>::type Iterator; typedef Iterator::value_type Handle; AccumulatorChainArray<Handle, Select<DataArg<1>, LabelArg<2>, Mean, Variance> > a; Iterator start = createCoupledIterator(data, labels); Iterator end = start.getEndIterator(); extractFeatures(start,end,a);
See Feature Accumulators for more information about feature computation via accumulators.
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© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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