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prapi Compound List

Here are the classes, structs, unions and interfaces with brief descriptions:
prapi::BeamSelectorBeamSelector implements the beam search optimization algorithm
prapi::ClassificationEvent< T, I, C >An event for informing interested listeners of the state of the classification
prapi::ClassificationExceptionClassificationException is thrown when an error occurs during a classification process
prapi::Classifier< T, I, C >Classifier is the common base class for all types of classifiers
prapi::Cluster< T, I, C >Cluster is a sample list with an additional sample as a cluster representative as used by the clustering algorithms
prapi::Color< T, comps >A universal representation of a color
prapi::ColorBmpCodecA codec for 24-bit color BMP images
prapi::ColorMap< T >ColorMap is a convenience class for creating color maps
prapi::ColorTransformContains methods for color space transforms that cannot be implemeted without some external knowledge
prapi::ColorViffCodec< T, comps >A codec for all kinds Color Viff images
prapi::ConfusionMatrixConfusionMatrix is a class for representing classification results
prapi::ConvolutionMask< T >ConvolutionMask is a matrix that can be used for image convolution operations (filtering)
prapi::Cumlog< T >Cumlog is a log-likelihood proximity measure
prapi::DistributionDistribution is a multi-dimensional list that can be accessed linearly as any list
prapi::EqualAreaQuantizerEqualAreaQuantizer derives the quantization from a distribution
prapi::EuclideanDistance< T >The standard Euclidean distance as a proximity measure
prapi::FeatureExtractionExceptionAn exception for errors in feature extraction
prapi::FeatureExtractor< T, U >FeatureExtractor is an interface for operations that calculate a feature vector out of something
prapi::FeatureScalingThis class contains static methods for scaling feature values
prapi::GaussianA class that contains methods for calculating values from Gaussian distributions and for creating various forms of a Gaussian function
prapi::GeneralHistogram< T >Produces a histogram from any matrix containing primitive data types by using a Quantizer
prapi::GlobalPeakDetector< T >GlobalPeakDetector is a class for detecting peaks in images, or in transform domains, like the Hough transform
prapi::HistogramIntersection< T >Histogram intersection calculates the intersection between two feature distributions
prapi::HistogramOperationA wrapper class for different types of histogram operations
prapi::HistogramOperation::ContrastStretchingMakes the ContrastStreching for a given Matrix
prapi::HistogramOperation::EqualizationMakes the Histogram Equalization for a given Matrix
prapi::HistogramOperation::ZNormalizationZ-normalizes an image
prapi::HSVColor< T >A class for the HSV (hue, saturation, value) color space
prapi::ImageImage is a static class which includes funtions for image analysis
prapi::ImageExceptionA common superclass for all exceptions that can be thrown when handling images
prapi::ImageSampleIdentifierA class used to identify samples taken from images
prapi::ImageTransform< T, U >ImageTransform is an interface for operations that convert images from one domain to another
prapi::ImageTransformExceptionAn exception for errors in transforming images
prapi::IndexerIndexer produces integer-valued matrices out of multi-channel color matrices or double-valued matrices
prapi::IntegerHistogramProduces a histogram from any matrix that holds integers
prapi::JDDistance< T >JDDistance (Jeffrey's Divergence) is a statistical dissimilarity measure
prapi::KernelStatic methods for creating kernels for various purposes
prapi::kNNClassifier< T, I, C >A k nearest neighbors classifier
prapi::LocalPeakDetectorAs the name implies, LocalPeakDetector detects peaks local in nature
prapi::MagickCodec< T >MagickCodec is a general-purpose image codec that uses the ImageMagick library to read and write images
prapi::MagickConverter< T >Converts ImageMagic images to a matrices
prapi::MagickConverter< Color< T, comps > >A specialization of the MagickConverter class for color images
prapi::MahalanobisClassifier< T, I, C >Mahalanobis classifier classifies unknown samples according to the Mahalanobis distance d2(si, mj) = (si - mj) Cj-1 (si - mj)T, where s and m are sample and model feature vectors, respectively
prapi::MinimumDistanceClassifier< T, I, C >An implementation of the minimum distance classifier
prapi::MultiClassifier< T, I >MultiClassifier combines class rankings from different classifiers and produces an overall result using a number of combination schemes
prapi::MultiDimensionalHistogramA feature extractor that produces multi-dimensional histogram out of a list of matrixs
prapi::MultiFeatureHistogramMultiFeatureHistogram produces many types of histograms out of layered feature matrices
prapi::MultiFeatureHistogram::ConcatenateThe Concatenate combiner concatenates histograms built separately for each layer
prapi::MultiFeatureHistogram::LayerCombinerLayerCombiner is an interface for different types of combination schemes
prapi::MultiFeatureHistogram::MergeThe Merge combiner merges histograms so that the pixels in all layers are collected to a single histogram
prapi::MultiFeatureHistogram::MultiDimensionalThe MultiDimensional combiner builds up a multi-dimensional histogram using the pixel values in each layer as coordinates in a multi-dimensional feature space
prapi::MultiFeatureHistogram::VQThe VQ combiner uses a VectorQuantizer to get a bin index for each multi-feature pixel
prapi::MultiFeatureProximity< T >MultifeatureProximity provides convenient means to combine different features in classification
prapi::NNClassifier< T, I, C >An implementation of the nearest neighbor classifier
prapi::OLVQ1< I, C >OLVQ1 implements the optimized-learning-rate learning vector quantization algorithm for training the code book of a vector quantizer
prapi::PnmCodec< T >A codec for PNM images
prapi::ProximityAdder< T >Adder is a wrap-up measure that just adds a constant value to the value provided by another measure
prapi::ProximityCombinerAn interface for classes that combine the proximities given by a set of different proximity measures
prapi::ProximityCombiner::MaximumA simple combiner that returns the maximum of the given proximities
prapi::ProximityCombiner::MinimumA simple combiner that returns the minimum of the given proximities
prapi::ProximityCombiner::SumA simple combiner that returns the sum of the given proximities
prapi::ProximityCombiner::WeightedSumA simple combiner that returns a weighted sum of the given proximities
prapi::ProximityExceptionProximityException is used if proximity calculation cannot be performed for some reason
prapi::ProximityMatrixProximityMatrix calculates the distances between all pairs of samples and places the results in a double-valued matrix
prapi::ProximityMeasure< T >ProximityMeasure is a general representation of a proximity measure between samples, sample sets (clusters) or between a sample and a cluster
prapi::ProximityModifier< operation, T >A general modifier for proximity measures
prapi::ProximityMultiplier< T >Multiplier is a wrap-up measure that multiplies the value provided by another measure by a constant value
prapi::QuantizationExceptionAn exception for quantization errors
prapi::QuantizerQuantizer is an interface specification for different types of quantization schemes
prapi::RandomA class for generating random numbers and performing randomization operations
prapi::RankCombinerRankCombiner is used by MultiClassifier in combining rankings obtained from multiple classifiers
prapi::RankCombiner::BordaCountThe Borda count is a simple method of combining classification ranks
prapi::RasterCodecRasterCodec reads and writes gray-scale images or integer-valued color images with a palette (colormap) in sunras format
prapi::RGBColor< T >A convenience class for the RGB color space
prapi::RGBColorImage< T >A class that provides methods for easily handling RGB color data
prapi::Sample< T, I, C >A template for a sample
prapi::SampleUtilsThe class SampleUtils includes fuction for handling and making sample sets
prapi::SelectionEventAn optimization event is fired for each completed iteration of an optimization method
prapi::SequentialSelectorMethods for sequential feature selection
prapi::SquaredEuclidean< T >Squared Euclidean distance as a proximity measure
prapi::SubSetSelectorA common base class for methods searching for sub-optimal subsets (of features, for example)
prapi::SubSetSelector::GoodnessMeasureAn interface for methods for evaluating the goodness of a subset (of features)
prapi::TreeClassificationEvent< T, I, C >An event for informing interested listeners of the state of the Tree Classification
prapi::TreeClassifier< T, I, C >TreeClassifier is made to help Building a treeclassifier
prapi::UniformQuantizerUniformQuantizer implements an uniform quantization scheme
prapi::VectorQuantizerVectorQuantizer is an interface for classes that are able to quantize a multi-dimensional feature space into a one-dimensional one
prapi::VQClassifier< I, C >VQClassifier is a vector quantizer that uses a code book and works as a classifier at the same time
prapi::VQExceptionA VQException is thrown when a vector quantizer cannot find a code vector index
prapi::XConverter< T >A class for converting gray-scale images to X images
prapi::XConverter< Color< T, 3 > >A class for converting color images to X images
prapi::XDisplayA C++ wrapper for the xlib's Display structure
prapi::XExceptionAn Exception for X windows related errors
prapi::XWindowsA class for displaying images on an X screen

Documentation generated on 11.09.2003 with Doxygen.
The documentation is copyrighted material.
Copyright © Topi Mäenpää 2003. All rights reserved.