Direct Graphical Models  v.1.5.2
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 1234]
 NDirectGraphicalModels
 Nfex
 CCCommonFeatureExtractorCommon class, which unites feature extraction algorithms
 CCCoordinateCoordinate feature extraction class
 CCDistanceDistance feature extraction class
 CCGlobalFeatureExtractorInterface class for global feature extraction algorithms
 CCGradientGradient feature extraction class
 CCHOGHOG (histogram of oriented gradients) feature extraction class
 CCHSVHue, Saturation and Value feature extraction class
 CCIntensityIntensity feature extraction class
 CCNDVINDVI (normalized difference vegetation index) feature extraction class
 CCScaleScale feature extraction class
 CCSIFTSIFT (scale-invariant feature transform) feature extraction class
 CCSparseCodingSparse Coding feature extraction class
 CCSparseDictionarySparse Dictionary Learning class
 CCVarianceVariance feature extraction class
 CIFeatureExtractorInterface class for feature extraction algorithms
 CILocalFeatureExtractorInterface class for local feature extraction algorithms
 CSqNeighbourhoodSquare neighborhood structure
 Nvis
 CCCameraControlTrackball camera control class
 CCMarkerMarker class
 CCMarkerHistogramHistogram Marker class
 CCTrackballCameraTrackball camera class
 CCBaseRandomModelBase abstract class for random model training
 CCCMatConfusion matrix class
 CCDecodeBase abstract class for random model decoding
 CCDecodeExactExact decoding class
 CCDiffFeaturesConcatenatorDifference features concatenator class
 CCFeaturesConcatenatorFeatures concatenator base abstract class
 CCGraphPairwise graph class
 CCGraph3Triple graph class
 CCGraphBoost
 CCGraphExtExtended Pairwise graph class
 CCGraphLayeredExtended Pairwise Layered graph class
 CCGraphWeissPairwise graph class
 CEdgeEdge structure
 CNodeNode structure
 CCInferBase abstract class for random model inference
 CCInferChainInference for chain graphs
 CCInferExactExact inference class
 CCInferLBPSum product Loopy Belief Propagation inference class
 CCInferTreeInference for tree graphs (undirected graphs without loops)
 CCInferTRWTree-reweighted inference class
 CCInferViterbiMax product Viterbi inference class
 CCKDNodeK-D Node class for the k-D Tree data structure
 CCKDTreeClass implementing k-D Tree data structure
 CCMessagePassingAbstract base class for message passing inference algorithmes
 CCNDGaussMulti - dimensional Gauss function class
 CCPDFGaussianGaissian-based PDF class
 CCPDFHistogramHistogram-based PDF class (1D)
 CCPDFHistogram2DHistogram-based PDF class (2D)
 CCPowellThe Powell search method class
 CCPriorBase abstract class for prior probability estimation
 CCPriorEdgeEdge prior probability estimation class
 CCPriorNodeNode prior probability estimation class
 CCPriorTripletTriplet prior probability estimation class
 CCRForestRandom Forest class
 CCSamplesAccumulatorSamples accumulator abstract class
 CCSimpleFeaturesConcatenatorSimple features concatenator class
 CCTrainEdgeBase abstract class for edge potentials training
 CCTrainEdgeConcatConcatenated edge training class
 CCTrainEdgePottsPotts edge training class
 CCTrainEdgePottsCSContrast-Sensitive Potts training class
 CCTrainEdgePriorContrast-Sensitive Potts training with edge prior probability class
 CCTrainLinkBase abstract class for link (inter-layer edge) potentials training
 CCTrainLinkNestedNested link (inter-layer edge) training class
 CCTrainNodeBase abstract class for node potentials training
 CCTrainNodeCvGMOpenCV Gaussian Model training class
 CCTrainNodeCvGMMOpenCV Gaussian Mixture Model training class
 CCTrainNodeCvRFOpenCV Random Forest training class
 CCTrainNodeGMGaussian Model training class
 CCTrainNodeGMMGaussian Mixture Model training class
 CCTrainNodeKNNNearest Neighbor training class
 CCTrainNodeMsRFMicrosoft Sherwood Random Forest training class
 CCTrainNodeNaiveBayesBayes training class
 CCTrainTripletBase abstract class for triplet potential training
 CEdgeEdge structure
 CIGraphInterface class for graphical models
 CIPDFInterface class for Probability Density Function (PDF)
 CITrainInterface class for random model training
 CNodeNode structure
 CTrainNodeCvGMMParamsOpenCV Random Forest parameters
 CTrainNodeCvRFParamsOpenCV Random Forest parameters
 CTrainNodeGMMParamsGaussian Mixture Model parameters
 CTrainNodeKNNParamsK-Nearest Neighbors parameters
 CTrainNodeMsRFParamsMicrosoft Research Random Forest parameters
 CTripletTriplet structure
 NMicrosoftResearch
 NCambridge
 NSherwood
 CForest