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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.
typedef DivideByCount < FlatScatterMatrix > | Covariance
| Alias. Covariance.
typedef DivideByCount < ScatterMatrixEigensystem > | CovarianceEigensystem
| Alias. Covariance eigensystem.
typedef DivideByCount< Sum > | Mean
| Alias. Mean.
typedef DivideByCount < SumOfAbsDifferences > | MeanAbsoluteDeviation
| Alias. Mean absolute deviation.
typedef Weighted< Coord < Principal< Variance > > > | MomentsOfInertia
| Alias. Moments of inertia.
typedef Coord< Principal < CoordinateSystem > > | RegionAxes
| Alias. Region axes.
typedef Coord< Mean > | RegionCenter
| Alias. Region center.
typedef Coord< Principal < StdDev > > | RegionRadii
| Alias. Region radii.
typedef RootDivideByCount < SumOfSquares > | RootMeanSquares
| Alias. Root mean square.
typedef SumOfSquaredDifferences | SSD
| Alias. Sum of squared differences.
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.
typedef DivideUnbiased < FlatScatterMatrix > | UnbiasedCovariance
| Alias. Unbiased covariance.
typedef RootDivideUnbiased < Central< PowerSum< 2 > > > | UnbiasedStdDev
| Alias. Unbiased standard deviation.
typedef DivideUnbiased < Central< PowerSum< 2 > > > | UnbiasedVariance
| Alias. Unbiased variance.
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)

## Detailed Description

This namespace contains the accumulator classes, fundamental statistics and modifiers. See Feature Accumulators for examples of usage.

## Function Documentation

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.

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)