prapi::BeamSelector | BeamSelector implements the beam search optimization algorithm |

prapi::ClassificationEvent< T, I, C > | An event for informing interested listeners of the state of the classification |

prapi::ClassificationException | ClassificationException 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::ColorBmpCodec | A codec for 24-bit color BMP images |

prapi::ColorMap< T > | ColorMap is a convenience class for creating color maps |

prapi::ColorTransform | Contains 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::ConfusionMatrix | ConfusionMatrix 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::Distribution | Distribution is a multi-dimensional list that can be accessed linearly as any list |

prapi::EqualAreaQuantizer | EqualAreaQuantizer derives the quantization from a distribution |

prapi::EuclideanDistance< T > | The standard Euclidean distance as a proximity measure |

prapi::FeatureExtractionException | An 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::FeatureScaling | This class contains static methods for scaling feature values |

prapi::Gaussian | A 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::HistogramOperation | A wrapper class for different types of histogram operations |

prapi::HistogramOperation::ContrastStretching | Makes the ContrastStreching for a given Matrix |

prapi::HistogramOperation::Equalization | Makes the Histogram Equalization for a given Matrix |

prapi::HistogramOperation::ZNormalization | Z-normalizes an image |

prapi::HSVColor< T > | A class for the HSV (hue, saturation, value) color space |

prapi::Image | Image is a static class which includes funtions for image analysis |

prapi::ImageException | A common superclass for all exceptions that can be thrown when handling images |

prapi::ImageSampleIdentifier | A 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::ImageTransformException | An exception for errors in transforming images |

prapi::Indexer | Indexer produces integer-valued matrices out of multi-channel color matrices or double-valued matrices |

prapi::IntegerHistogram | Produces a histogram from any matrix that holds integers |

prapi::JDDistance< T > | JDDistance (Jeffrey's Divergence) is a statistical dissimilarity measure |

prapi::Kernel | Static methods for creating kernels for various purposes |

prapi::kNNClassifier< T, I, C > | A k nearest neighbors classifier |

prapi::LocalPeakDetector | As 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 d^{2}(s_{i}, m_{j}) = (s_{i} - m_{j}) C_{j}^{-1} (s_{i} - m_{j})^{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::MultiDimensionalHistogram | A feature extractor that produces multi-dimensional histogram out of a list of matrixs |

prapi::MultiFeatureHistogram | MultiFeatureHistogram produces many types of histograms out of layered feature matrices |

prapi::MultiFeatureHistogram::Concatenate | The Concatenate combiner concatenates histograms built separately for each layer |

prapi::MultiFeatureHistogram::LayerCombiner | LayerCombiner is an interface for different types of combination schemes |

prapi::MultiFeatureHistogram::Merge | The Merge combiner merges histograms so that the pixels in all layers are collected to a single histogram |

prapi::MultiFeatureHistogram::MultiDimensional | The 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::VQ | The 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::ProximityCombiner | An interface for classes that combine the proximities given by a set of different proximity measures |

prapi::ProximityCombiner::Maximum | A simple combiner that returns the maximum of the given proximities |

prapi::ProximityCombiner::Minimum | A simple combiner that returns the minimum of the given proximities |

prapi::ProximityCombiner::Sum | A simple combiner that returns the sum of the given proximities |

prapi::ProximityCombiner::WeightedSum | A simple combiner that returns a weighted sum of the given proximities |

prapi::ProximityException | ProximityException is used if proximity calculation cannot be performed for some reason |

prapi::ProximityMatrix | ProximityMatrix 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::QuantizationException | An exception for quantization errors |

prapi::Quantizer | Quantizer is an interface specification for different types of quantization schemes |

prapi::Random | A class for generating random numbers and performing randomization operations |

prapi::RankCombiner | RankCombiner is used by MultiClassifier in combining rankings obtained from multiple classifiers |

prapi::RankCombiner::BordaCount | The Borda count is a simple method of combining classification ranks |

prapi::RasterCodec | RasterCodec 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::SampleUtils | The class SampleUtils includes fuction for handling and making sample sets |

prapi::SelectionEvent | An optimization event is fired for each completed iteration of an optimization method |

prapi::SequentialSelector | Methods for sequential feature selection |

prapi::SquaredEuclidean< T > | Squared Euclidean distance as a proximity measure |

prapi::SubSetSelector | A common base class for methods searching for sub-optimal subsets (of features, for example) |

prapi::SubSetSelector::GoodnessMeasure | An 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::UniformQuantizer | UniformQuantizer implements an uniform quantization scheme |

prapi::VectorQuantizer | VectorQuantizer 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::VQException | A 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::XDisplay | A C++ wrapper for the xlib's Display structure |

prapi::XException | An Exception for X windows related errors |

prapi::XWindows | A 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.