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Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 1234]
 CAbsPowerSum< N >Basic statistic. AbsPowerSum<N> = $ \sum_i |x_i|^N $
 CAccumulatorChain< T, Selected, dynamic >Create an accumulator chain containing the selected statistics and their dependencies
 CAccumulatorChain< CoupledArrays< N, T1, T2, T3, T4, T5 >::HandleType, Selected, dynamic >
 CAccumulatorChain< CoupledArrays< N, T1, T2, T3, T4, T5 >::HandleType, Selected, true >
 CAccumulatorChain< CoupledHandleType< N >::type, SELECT >
 CAccumulatorChain< CoupledHandleType< N, T >::type, SELECT >
 CAccumulatorChain< T, Selected, true >
 CAccumulatorChainArray< T, Selected, dynamic >Create an array of accumulator chains containing the selected per-region and global statistics and their dependencies
 CAccumulatorChainArray< CoupledArrays< N, T1, T2, T3, T4, T5 >::HandleType, Selected, dynamic >
 CAccumulatorChainArray< CoupledArrays< N, T1, T2, T3, T4, T5 >::HandleType, Selected, true >
 CAccumulatorChainArray< T, Selected, true >
 CAdjacencyListGraphUndirected adjacency list graph in the LEMON API
 CAffineMotionEstimationOptions< SPLINEORDER >Option object for affine registration functions
 CAnyTypesafe storage of arbitrary values
 CArgMaxWeightBasic statistic. Data where weight assumes its maximal value
 CArgMinWeightBasic statistic. Data value where weight assumes its minimal value
 CArrayOfRegionStatistics< RegionStatistics, LabelType >Calculate statistics for all regions of a labeled image
 CArrayVectorView< T >
 CArrayVectorView< arc_descriptor >
 CArrayVectorView< ArrayVector< bool > >
 CArrayVectorView< ArrayVector< edge_descriptor > >
 CArrayVectorView< ArrayVector< MultiArrayIndex > >
 CArrayVectorView< AxisInfo >
 CArrayVectorView< BinType >
 CArrayVectorView< bool >
 CArrayVectorView< char >
 CArrayVectorView< ClassLabelType >
 CArrayVectorView< DecisionTree_t >
 CArrayVectorView< detail::DecisionTreeDeprec >
 CArrayVectorView< double >
 CArrayVectorView< hsize_t >
 CArrayVectorView< ImageType >
 CArrayVectorView< index_type >
 CArrayVectorView< IndexType >
 CArrayVectorView< INT >
 CArrayVectorView< int >
 CArrayVectorView< Int32 >
 CArrayVectorView< Label >
 CArrayVectorView< Label_t >
 CArrayVectorView< MultiArrayIndex >
 CArrayVectorView< NeighborOffsetArray >
 CArrayVectorView< Node >
 CArrayVectorView< npy_intp >
 CArrayVectorView< Permutation< 1 > >
 CArrayVectorView< Permutation< N > >
 CArrayVectorView< point_type >
 CArrayVectorView< RegionAccumulatorChain >
 CArrayVectorView< Segment >
 CArrayVectorView< shape_type >
 CArrayVectorView< SharedChunkHandle< N, T > * >
 CArrayVectorView< size_t >
 CArrayVectorView< std::pair< Int32, double > >
 CArrayVectorView< std::ptrdiff_t >
 CArrayVectorView< std::queue< ValueType > >
 CArrayVectorView< TinyVector< double, 2 > >
 CArrayVectorView< unsigned char >
 CArrayVectorView< vigra::ArrayVector< index_type > >
 CArrayVectorView< vigra::ArrayVector< typename out_edge_iterator::value_type > >
 CArrayVectorView< vigra::linalg::Matrix< Complex > >
 CArrayVectorView< vigra::TinyVector >
 CAutoRangeHistogram< BinCount >Histogram where range mapping bounds are defined by minimum and maximum of data
 CBasicImage< PIXELTYPE, Alloc >Fundamental class template for images
 CBasicImage< double >
 CBasicImage< float >
 CBasicImage< InternalValue >
 CBasicImage< TinyVector< double, 2 > >
 CBasicImage< value_type >
 CBasicImage< VALUETYPE >
 CBasicImageIteratorBase< IMAGEITERATOR, PIXELTYPE, REFERENCE, POINTER, LINESTARTITERATOR >
 CBasicImageIteratorBase< BasicImageIterator< PIXELTYPE, ITERATOR >, PIXELTYPE, PIXELTYPE &, PIXELTYPE *, ITERATOR >
 CBasicImageIteratorBase< ConstBasicImageIterator< PIXELTYPE, ITERATOR >, PIXELTYPE, PIXELTYPE const &, PIXELTYPE const *, ITERATOR >
 CBasicImageView< PIXELTYPE >BasicImage using foreign memory
 CBestGiniOfColumn< LineSearchLossTag >
 CBestGiniOfColumn< vigra::GiniCriterion >
 CBilinearInterpolatingAccessor< ACCESSOR, VALUETYPE >Bilinear interpolation at non-integer positions
 CBinaryForestBinaryForest stores a collection of rooted binary trees
 CBlueAccessor< RGBVALUE >
 CBox< VALUETYPE, DIMENSION >Represent an n-dimensional box as a (begin, end) pair. Depending on the value type, end() is considered to be outside the box (as in the STL, for integer types), or inside (for floating point types). size() will always be end() - begin()
 CBox< PointValue, DIM >
 CBrightnessContrastFunctor< PixelType >Adjust brightness and contrast of an image
 CBrightnessContrastFunctor< ComponentType >
 CBrightnessContrastFunctor< unsigned char >
 CBSplineBase< ORDER, T >
 CBSplineBase< ORDER, double >
 CBucketQueue< ValueType, Ascending >Priority queue implemented using bucket sort
 CBucketQueue< ValueType, Ascending >
 CBucketQueue< ValueType, false >
 CCatmullRomSpline< T >
 CCentral< TAG >Modifier. Substract mean before computing statistic
 CCentral< PowerSum< 2 > >Spezialization: works in pass 1, operator+=() supported (merging supported)
 CCentral< PowerSum< 3 > >Specialization: works in pass 2, operator+=() supported (merging supported)
 CCentral< PowerSum< 4 > >Specialization: works in pass 2, operator+=() supported (merging supported)
 CCentralMoment< N >Alias. CentralMoment<N>
 CChangeablePriorityQueue< T, COMPARE >Heap-based changable priority queue with a maximum number of elemements
 CChangeablePriorityQueue< ValueType >
 CChangeablePriorityQueue< WeightType >
 CChunkedArray< N, T >Interface and base class for chunked arrays
 CChunkedArrayOptionsOption object for ChunkedArray construction
 CChunkedArrayTag
 Ccl_charNAccessor_COMP
 Ccl_TYPE3WriteAccessor_s1
 Ccl_TYPE3WriteAccessor_s2
 CClusteringOptionsOptions object for hierarchical clustering
 CColumnIterator< IMAGE_ITERATOR >Iterator adapter to linearly access columns
 CConstValueIterator< PIXELTYPE >Iterator that always returns the constant specified in the constructor
 CConvexHullCompute the convex hull of a region
 CConvexHullFeaturesCompute object features related to the convex hull
 CConvolutionOptions< dim >Options class template for convolutions
 CConvolutionOptions< DIM >
 CConvolutionOptions< N >
 CCoord< TAG >Modifier. Compute statistic from pixel coordinates rather than from pixel values
 CCoordinateConstValueAccessor< Accessor, COORD >Forward accessor to the value() part of the values an iterator points to
 CCoordinateSystemBasic statistic. Identity matrix of appropriate size
 CCorrectStatus
 CCoscotFunction< T >
 CCountingIterator< T >Iterator that counts upwards or downwards with a given step size
 CCoupledHandle< T, NEXT >
 CCoupledIteratorType< N, T1, T2, T3, T4, T5 >
 CCoupledIteratorType< N, void, void, void, void, void >
 CCoupledScanOrderIterator< N, HANDLES, DIMENSION >Iterate over multiple images simultaneously in scan order
 CCoupledScanOrderIterator< N >
 CCrackContourCirculator< IMAGEITERATOR >Circulator that walks around a given region
 CDataArg< INDEX >Specifies index of data in CoupledHandle
 CDepthStopRandom forest 'maximum depth' stop criterion
 CDiff2DTwo dimensional difference vector
 CDiffusivityFunctor< Value >Diffusivity functor for non-linear diffusion
 CDist2D
 CDistancePowerFunctor< N >
 CDivideByCount< TAG >Modifier. Divide statistic by Count: DivideByCount<TAG> = TAG / Count
 CDivideUnbiased< TAG >Modifier. Divide statistics by Count-1: DivideUnbiased<TAG> = TAG / (Count-1)
 CDraw< T1, T2, C1, C2 >
 CDT_StackEntry< Iter >
 CDualVector< T, N >
 CEarlyStoppStdStandard early stopping criterion
 CEdgel
 CEdgeWeightNodeFeatures< MERGE_GRAPH, EDGE_INDICATOR_MAP, EDGE_SIZE_MAP, NODE_FEATURE_MAP, NODE_SIZE_MAP, MIN_WEIGHT_MAP, NODE_LABEL_MAP >This Cluster Operator is a MONSTER. It can really do a lot
 CEnhancedFrostFunctor< VALUETYPE >This function tries to reduce the speckle noise of an image by applying the Enhanced Frost filter
 CEnhancedLeeFunctor< VALUETYPE >This function tries to reduce the speckle noise of an image by applying the Enhanced Lee filter
 CEntropyCriterion
 CEntropyScoreFunctor that computes the entropy score
 CFFTWComplex< Real >Wrapper class for the FFTW complex types 'fftw_complex'
 CFFTWComplex< Real >
 CFFTWConvolvePlan< N, Real >
 CFFTWImaginaryAccessor< Real >
 CFFTWLogMagnitudeAccessor< Real >
 CFFTWMagnitudeAccessor< Real >
 CFFTWPhaseAccessor< Real >
 CFFTWPlan< N, Real >
 CFFTWPlan< N, Real >
 CFFTWRealAccessor< Real >
 CFFTWRealAccessor< double >
 CFFTWSquaredMagnitudeAccessor< Real >
 CFilterIterator< PREDICATE, ITER >This iterator creates a view of another iterator and skips elements that do not fulfill a given predicate
 CFindAverage< VALUETYPE >Find the average pixel value in an image or ROI
 CFindAverageAndVariance< VALUETYPE >Find the average pixel value and its variance in an image or ROI
 CFindBoundingRectangleCalculate the bounding rectangle of an ROI in an image
 CFindMinMax< VALUETYPE >Find the minimum and maximum pixel value in an image or ROI
 CFindROISize< VALUETYPE >Calculate the size of an ROI in an image
 CFindSum< VALUETYPE >Find the sum of the pixel values in an image or ROI
 CFirstSeenBasic statistic. First data value seen of the object
 CFixedPoint< IntBits, FractionalBits >
 CFixedPoint16< IntBits, OverflowHandling >
 CFlatScatterMatrixBasic statistic. Flattened uppter-triangular part of scatter matrix
 CFunctorTraits< T >Export associated information for a functor
 CGammaFunctor< PixelType >Perform gamma correction of an image
 CGammaFunctor< ComponentType >
 CGammaFunctor< unsigned char >
 CGammaMAPFunctor< VALUETYPE >This function tries to reduce the speckle noise of an image by applying the Gamma Maximum A Posteriori (MAP) filter
 CGaussian< T >
 CGetClusterVariables
 CGiniCriterion
 CGiniScoreFunctor that computes the gini score
 CGlobal< TAG >Modifier. Compute statistic globally rather than per region
 CGlobalRangeHistogram< BinCount >Like AutoRangeHistogram, but use global min/max rather than region min/max
 CGrayToRGBAccessor< VALUETYPE >
 CGreenAccessor< RGBVALUE >
 CGridGraph< N, DirectedTag >Define a grid graph in arbitrary dimensions
 CGridGraph< N, undirected_tag >
 CHC_Entry
 CHClustering
 CHDF5DisableErrorOutputTemporarily disable HDF5's native error output
 CHDF5FileAccess to HDF5 files
 CHDF5HandleWrapper for unique hid_t objects
 CHDF5HandleSharedWrapper for shared hid_t objects
 CHDF5ImportInfoArgument object for the function readHDF5()
 CHierarchicalIteratorType< N, T1, T2, T3, T4, T5 >
 CHistogramOptionsSet histogram options
 CImageArray< ImageType, Alloc >Fundamental class template for arrays of equal-sized images
 CImageArray< ImageType, typename std::allocator_traits< typename ImageType::allocator_type >::template rebind_alloc< ImageType > >
 CImageExportInfoArgument object for the function exportImage()
 CImageImportInfoArgument object for the function importImage()
 CImageIteratorBase< IMAGEITERATOR, PIXELTYPE, REFERENCE, POINTER, StridedOrUnstrided >Base class for 2D random access iterators
 CImageIteratorBase< ConstImageIterator< PIXELTYPE >, PIXELTYPE, PIXELTYPE const &, PIXELTYPE const * >
 CImageIteratorBase< ConstStridedImageIterator< PIXELTYPE >, PIXELTYPE, PIXELTYPE const &, PIXELTYPE const *, StridedArrayTag >
 CImageIteratorBase< ImageIterator< PIXELTYPE >, PIXELTYPE, PIXELTYPE &, PIXELTYPE * >
 CImageIteratorBase< StridedImageIterator< PIXELTYPE >, PIXELTYPE, PIXELTYPE &, PIXELTYPE *, StridedArrayTag >
 CImagePyramid< ImageType, Alloc >Class template for logarithmically tapering image pyramids
 CConvexHullFeatures::Impl< T, BASE >Result type of the covex hull feature calculation
 CGridGraph< N, DirectedTag >::InDegMapType of a property map that returns the number of incoming edges of a given node (API: LEMON, use via lemon::InDegMap<Graph>)
 CGridGraph< N, DirectedTag >::IndexMapType of a property map that returns the coordinate of a given node (API: LEMON)
 CIntegerHistogram< BinCount >Histogram where data values are equal to bin indices
 CIterablePartition< T >
 CIterablePartition< IdType >
 CIteratorAdaptor< Policy >Quickly create 1-dimensional iterator adapters
 CIteratorTraits< T >Export associated information for each image iterator
 CKernel1D< ARITHTYPE >Generic 1 dimensional convolution kernel
 CKernel2D< ARITHTYPE >Generic 2 dimensional convolution kernel
 CKolmogorovSmirnovScoreFunctor that computes the Kolmogorov-Smirnov score
 CKuanFunctor< VALUETYPE >This function tries to reduce the speckle noise of an image by applying the Kuan filter
 CKurtosisBasic statistic. Kurtosis
 CLab2RGBFunctor< T >Convert perceptual uniform CIE L*a*b* into linear (raw) RGB
 CLab2RGBPrimeFunctor< T >Convert perceptual uniform CIE L*a*b* into non-linear (gamma corrected) R'G'B'
 CLab2XYZFunctor< T >Convert perceptual uniform CIE L*a*b* into standardized tri-stimulus XYZ
 CLab2XYZFunctor< component_type >
 CLabelArg< INDEX >Specifies index of labels in CoupledHandle
 CLabelOptionsOption object for labelMultiArray()
 CLastValueFunctor< VALUETYPE >Stores and returns the last value it has seen
 CLeastAngleRegressionOptionsPass options to leastAngleRegression()
 CLeeFunctor< VALUETYPE >This function tries to reduce the speckle noise of an image by applying the basic Lee filter
 CLineIterator< IMAGE_ITERATOR >Iterator adapter to iterate along an arbitrary line on the image
 CLocalMinmaxOptionsOptions object for localMinima() and localMaxima()
 CLuv2RGBFunctor< T >Convert perceptual uniform CIE L*u*v* into linear (raw) RGB
 CLuv2RGBPrimeFunctor< T >Convert perceptual uniform CIE L*u*v* into non-linear (gamma corrected) R'G'B'
 CLuv2XYZFunctor< T >Convert perceptual uniform CIE L*u*v* into standardized tri-stimulus XYZ
 CLuv2XYZFunctor< component_type >
 CMagnitudeFunctor< ValueType >
 CMaximumBasic statistic. Maximum value
 CMedian
 CMergeGraphAdaptor< GRAPH >Undirected graph adaptor for edge contraction and feature merging
 CMeshGridAccessor
 CMetric< T >Functor to compute a metric between two vectors
 CMetric< float >
 CMinimumBasic statistic. Minimum value
 CMoment< N >Alias. Moment<N>
 CMultiArrayNavigator< MULTI_ITERATOR, N >A navigator that provides access to the 1D subranges of an n-dimensional range given by a vigra::MultiIterator and an nD shape
 CMultiArrayShape< N >
 CMultiArrayShape< 2 >
 CMultiArrayShape< actual_dimension >
 CMultiArrayShape< dim >
 CMultiArrayShape< Dimensions >
 CMultiArrayView< N, T, StrideTag >Base class for, and view to, vigra::MultiArray
 CMultiArrayView< 1, int >
 CMultiArrayView< 1, T >
 CMultiArrayView< 1, T, StridedArrayTag >
 CMultiArrayView< 2, double >
 CMultiArrayView< 2, int >
 CMultiArrayView< 2, LabelInt >
 CMultiArrayView< 2, T, C1 >
 CMultiArrayView< 2, T, C2 >
 CMultiArrayView< 2, T1, C1 >
 CMultiArrayView< 2, T1, UnstridedArrayTag >
 CMultiArrayView< 2, T2, C2 >
 CMultiArrayView< 2, T2, UnstridedArrayTag >
 CMultiArrayView< 2, UInt8 >
 CMultiArrayView< 2, VALUETYPE, StridedOrUnstrided >
 CMultiArrayView< DIM, PixelTypeIn >
 CMultiArrayView< DIM, RealPromotePixelType >
 CMultiArrayView< DIM, RealPromoteScalarType >
 CMultiArrayView< IntrinsicGraphShape< Graph >::IntrinsicEdgeMapDimension, Value >
 CMultiArrayView< IntrinsicGraphShape< Graph >::IntrinsicNodeMapDimension, Value >
 CMultiArrayView< N, NumpyArrayTraits< N, T, StridedArrayTag >::value_type, StridedArrayTag >
 CMultiArrayView< N, Real, UnstridedArrayTag >
 CMultiArrayView< N, T, S >
 CMultiArrayView< N, T, UnstridedArrayTag >
 CMultiArrayView< N, UnqualifiedType< U >::type >
 CMultiArrayView< N, vigra::detail::ResolveMultiband< T >::type, vigra::detail::ResolveMultiband< T >::Stride >
 CMultiBlocking< DIM, C >
 CMultiCoordinateNavigator< Dimensions, N >A navigator that provides access to the 1D subranges of an n-dimensional range given by an nD shape
 CMultiImageAccessor2< Iter1, Acc1, Iter2, Acc2 >Access two images simultaneously
 CMultiIterator< N, T, REFERENCE, POINTER >A multi-dimensional hierarchical iterator to be used with vigra::MultiArrayView if it is not strided
 CNeighborCodeEncapsulation of direction management for the 8-neighborhood
 CNeighborCodeEncapsulation of direction management for 4-neighborhood
 CNeighborCode3DEncapsulation of direction management of neighbors for a 3D 6-neighborhood
 CNeighborCode3DEncapsulation of direction management of neighbors for a 3D 26-neighborhood
 CNeighborhoodCirculator< IMAGEITERATOR, NEIGHBORCODE >Circulator that walks around a given location in a given image
 CNeighborhoodCirculator< IMAGEITERATOR, EightNeighborCode >
 CNeighborOffsetCirculator< NEIGHBORCODE >Circulator that walks around a given location
 CNodeBase
 CNodeComplexityStopRandom forest 'node complexity' stop criterion
 CNoiseNormalizationOptionsPass options to one of the noise normalization functions
 CNonlinearLSQOptionsPass options to nonlinearLeastSquares()
 CNormalizeStatus
 CNormalRandomFunctor< Engine >
 CNumInstancesStopRandom forest 'number of datapoints' stop criterion
 CNumpyAnyArray
 CGridGraph< N, DirectedTag >::OutDegMapType of a property map that returns the number of outgoing edges of a given node (API: LEMON, use via lemon::OutDegMap<Graph>)
 CParallelOptionsOption base class for parallel algorithms
 CPermuteCluster< Iter, DT >
 CPLSAOptionsOption object for the pLSA algorithm
 CPolynomialView< T >
 CPolytope< N, T >Represent an n-dimensional polytope
 CPolytope< N, double >
 CPowerSum< N >Basic statistic. PowerSum<N> = $ \sum_i x_i^N $
 CPrincipal< TAG >Modifier. Project onto PCA eigenvectors
 CPrincipal< CoordinateSystem >Specialization (covariance eigenvectors): works in pass 1, operator+=() supported (merging)
 CPrincipal< PowerSum< 2 > >Specialization (covariance eigenvalues): works in pass 1, operator+=() supported (merging)
 CPriorityQueue< ValueType, PriorityType, Ascending >Heap-based priority queue compatible to BucketQueue
 CProblemSpec< LabelType >Problem specification class for the random forest
 CProblemSpec< double >
 CProblemSpec< LabelType >
 CProcessor< Tag, LabelType, T1, C1, T2, C2 >
 CProcessor< ClassificationTag, LabelType, T1, C1, T2, C2 >
 CProcessor< RegressionTag, LabelType, T1, C1, T2, C2 >
 CPropertyMap< KEYTYPE, MAPPEDTYPE, ContainerTag >The PropertyMap is used to store Node or Arc information of graphs
 CPropertyMap< KEYTYPE, MAPPEDTYPE, IndexVectorTag >Specialization of PropertyMap that stores the elements in a vector (size = number of stored elements). An additional index vector is needed for bookkeeping (size = max node id of stored elements)
 CPropertyMap< KEYTYPE, MAPPEDTYPE, VectorTag >Specialization of PropertyMap that stores the elements in a vector (size = max node id of stored elements)
 CPurityStopRandom forest 'node purity' stop criterion
 CQuaternion< ValueType >
 CRandomForest< LabelType, PreprocessorTag >Random forest version 2 (see also vigra::rf3::RandomForest for version 3)
 CRandomForest< FEATURES, LABELS, SPLITTESTS, ACCTYPE >Random forest version 3
 CRandomForestClassCounter< DataSource, CountArray >
 CRandomForestOptionsOptions object for the random forest
 CRandomForestOptionsOptions class for vigra::rf3::RandomForest version 3
 CRandomNumberGenerator< Engine >
 CRandomSplitOfColumn
 CRangeReturn both the minimum and maximum in std::pair
 CRational< IntType >
 CRect2DTwo dimensional rectangle
 CRedAccessor< RGBVALUE >
 CReduceFunctor< FUNCTOR, VALUETYPE >Apply a functor to reduce the dimensionality of an array
 CRegionCircularityCompute the circularity of a 2D region
 CRegionContourCompute the contour of a 2D region
 CRegionEccentricityCompute the eccentricity of a 2D region in terms of its prinipal radii
 CRegionPerimeterCompute the perimeter of a 2D region
 CRFErrorCallback
 CRFVisitorBaseBase class from which all random forest visitors derive
 CRGB2LabFunctor< T >Convert linear (raw) RGB into perceptual uniform CIE L*a*b*
 CRGB2LuvFunctor< T >Convert linear (raw) RGB into perceptual uniform CIE L*u*v*
 CRGB2RGBPrimeFunctor< From, To >Convert linear (raw) RGB into non-linear (gamma corrected) R'G'B'
 CRGB2sRGBFunctor< From, To >Convert linear (raw) RGB into standardized sRGB
 CRGB2XYZFunctor< T >Convert linear (raw) RGB into standardized tri-stimulus XYZ
 CRGBGradientMagnitudeFunctor< ValueType >
 CRGBPrime2LabFunctor< T >Convert non-linear (gamma corrected) R'G'B' into perceptual uniform CIE L*a*b*
 CRGBPrime2LuvFunctor< T >Convert non-linear (gamma corrected) R'G'B' into perceptual uniform CIE L*u*v*
 CRGBPrime2RGBFunctor< From, To >Convert non-linear (gamma corrected) R'G'B' into non-linear (raw) RGB
 CRGBPrime2XYZFunctor< T >Convert non-linear (gamma corrected) R'G'B' into standardized tri-stimulus XYZ
 CRGBPrime2YPrimeCbCrFunctor< T >Convert non-linear (gamma corrected) R'G'B' into Y'CbCr color difference components
 CRGBPrime2YPrimeIQFunctor< T >Convert non-linear (gamma corrected) R'G'B' into Y'IQ components
 CRGBPrime2YPrimePbPrFunctor< T >Convert non-linear (gamma corrected) R'G'B' into Y'PbPr color difference components
 CRGBPrime2YPrimeUVFunctor< T >Convert non-linear (gamma corrected) R'G'B' into Y'UV components
 CRGBToGrayAccessor< RGBVALUE >
 CRootDivideByCount< TAG >Modifier. RootDivideByCount<TAG> = sqrt( TAG/Count )
 CRootDivideUnbiased< TAG >Modifier. RootDivideUnbiased<TAG> = sqrt( TAG / (Count-1) )
 CRowIterator< IMAGE_ITERATOR >Iterator adapter to linearly access row
 CSampler< Random >Create random samples from a sequence of indices
 CSamplerOptionsOptions object for the Sampler class
 CScatterMatrixEigensystem
 CSeedOptionsOptions object for generateWatershedSeeds()
 CSeedRgDirectValueFunctor< Value >Statistics functor to be used for seeded region growing
 CSelect< T01, T02, T03, T04, T05, T06, T07, T08, T09, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20 >Wrapper for MakeTypeList that additionally performs tag standardization
 CShortestPathDijkstra< GRAPH, WEIGHT_TYPE >Shortest path computer
 CSIFImportInfoExtracts image properties from an Andor SIF file header
 CSkeletonOptionsOption object for skeletonizeImage()
 CSkewnessBasic statistic. Skewness
 CSlantedEdgeMTFOptionsPass options to one of the slantedEdgeMTF() functions
 CSlicOptionsOptions object for slicSuperpixels()
 CSortSamplesByDimensions< DataMatrix >
 CSplice< T >
 CSplineImageView< ORDER, VALUETYPE >Create a continuous view onto a discrete image using splines
 CSplineImageView0< VALUETYPE, INTERNAL_TRAVERSER >Create an image view for nearest-neighbor interpolation
 CSplineImageView1< VALUETYPE, INTERNAL_TRAVERSER >Create an image view for bi-linear interpolation
 CSplitBase< Tag >
 CSplitBase< ClassificationTag >
 CsRGB2RGBFunctor< From, To >Convert standardized sRGB into non-linear (raw) RGB
 CStandardAccessor< VALUETYPE >Encapsulate access to the values an iterator points to
 CStandardAccessor< RGBVALUE >
 CStandardAccessor< SEQUENCE >
 CStandardAccessor< VECTOR >
 CStandardConstAccessor< VALUETYPE >Encapsulate read access to the values an iterator points to
 CStandardConstValueAccessor< VALUETYPE >Encapsulate access to the values an iterator points to
 CStandardQuantiles< HistogramAccumulator >Compute (0%, 10%, 25%, 50%, 75%, 90%, 100%) quantiles from given histogram
 CStandardValueAccessor< VALUETYPE >Encapsulate access to the values an iterator points to
 CStopBase
 CStridedArrayTag
 CStridedMultiIterator< N, T, REFERENCE, POINTER >A multi-dimensional hierarchical iterator to be used with vigra::MultiArrayView if it is not strided
 CThinPlateSplineFunctor
 CThreadPoolThread pool class to manage a set of parallel workers
 CThreshold< SrcValueType, DestValueType >Threshold an image
 CTinyVectorBase< VALUETYPE, SIZE, DATA, DERIVED >Base class for fixed size vectors
 CTinyVectorBase< double, SIZE, double[SIZE], TinyVector< double, SIZE > >
 CTinyVectorBase< float, SIZE, float[SIZE], TinyVector< float, SIZE > >
 CTinyVectorBase< INDEX_TYPE, SIZE, INDEX_TYPE[SIZE], TinyVector< INDEX_TYPE, SIZE > >
 CTinyVectorBase< int, SIZE, int[SIZE], TinyVector< int, SIZE > >
 CTinyVectorBase< PointValue, SIZE, PointValue[SIZE], TinyVector< PointValue, SIZE > >
 CTinyVectorBase< T, SIZE, T *, TinyVectorView< T, SIZE > >
 CTinyVectorBase< T, SIZE, T[SIZE], TinyVector< T, SIZE > >
 CTinyVectorBase< unsigned int, SIZE, unsigned int[SIZE], TinyVector< unsigned int, SIZE > >
 CTinyVectorBase< ValueType, SIZE, ValueType[SIZE], TinyVector< ValueType, SIZE > >
 CTinyVectorBase< VALUETYPE, SIZE, VALUETYPE[SIZE], TinyVector< VALUETYPE, SIZE > >
 CGridGraph< N, DirectedTag >::traversal_categoryDefine several graph tags related to graph traversal (API: boost::graph, use via boost::graph_traits<Graph>::traversal_category)
 CUnbiasedKurtosisBasic statistic. Unbiased Kurtosis
 CUnbiasedSkewnessBasic statistic. Unbiased Skewness
 CUniformIntRandomFunctor< Engine >
 CUniformRandomFunctor< Engine >
 CUnstridedArrayTag
 CUserRangeHistogram< BinCount >Histogram where user provides bounds for linear range mapping from values to indices
 CVariableSelectionResult
 CVectorComponentAccessor< VECTORTYPE >Accessor for one component of a vector
 CVectorComponentValueAccessor< VECTORTYPE >Accessor for one component of a vector
 CVectorElementAccessor< ACCESSOR >Accessor for one component of a vector
 CVectorNormFunctor< ValueType >A functor for computing the vector norm
 CVectorNormSqFunctor< ValueType >A functor for computing the squared vector norm
 CVisitorBase
 CVisitorCopy< VISITOR >
 CVisitorNode< Visitor, Next >
 CVolumeExportInfoArgument object for the function exportVolume()
 CVolumeImportInfoArgument object for the function importVolume()
 CWatershedOptionsOptions object for watershed algorithms
 CWeightArg< INDEX >Specifies index of data in CoupledHandle
 CWeighted< TAG >Compute weighted version of the statistic
 CWignerMatrix< Real >Computation of Wigner D matrix + rotation functions in SH,VH and R³
 CXYZ2LabFunctor< T >Convert standardized tri-stimulus XYZ into perceptual uniform CIE L*a*b*
 CXYZ2LabFunctor< component_type >
 CXYZ2LuvFunctor< T >Convert standardized tri-stimulus XYZ into perceptual uniform CIE L*u*v*
 CXYZ2LuvFunctor< component_type >
 CXYZ2RGBFunctor< T >Convert standardized tri-stimulus XYZ into linear (raw) RGB
 CXYZ2RGBPrimeFunctor< T >Convert standardized tri-stimulus XYZ into non-linear (gamma corrected) R'G'B'
 CYPrimeCbCr2RGBPrimeFunctor< T >Convert Y'CbCr color difference components into non-linear (gamma corrected) R'G'B'
 CYPrimeIQ2RGBPrimeFunctor< T >Convert Y'IQ color components into non-linear (gamma corrected) R'G'B'
 CYPrimePbPr2RGBPrimeFunctor< T >Convert Y'PbPr color difference components into non-linear (gamma corrected) R'G'B'
 CYPrimeUV2RGBPrimeFunctor< T >Convert Y'UV color components into non-linear (gamma corrected) R'G'B'

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

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