Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 1234]

▼NDirectGraphicalModels
▼Nfex
CCCommonFeatureExtractor	Common class, which unites feature extraction algorithms
CCCoordinate	Coordinate feature extraction class
CCDistance	Distance feature extraction class
CCGlobalFeatureExtractor	Interface class for global feature extraction algorithms
CCGradient	Gradient feature extraction class
CCHOG	HOG (histogram of oriented gradients) feature extraction class
CCHSV	Hue, Saturation and Value feature extraction class
CCIntensity	Intensity feature extraction class
CCNDVI	NDVI (normalized difference vegetation index) feature extraction class
CCScale	Scale feature extraction class
CCSIFT	SIFT (scale-invariant feature transform) feature extraction class
CCSparseCoding	Sparse Coding feature extraction class
CCSparseDictionary	Sparse Dictionary Learning class
CCVariance	Variance feature extraction class
CIFeatureExtractor	Interface class for feature extraction algorithms
CILocalFeatureExtractor	Interface class for local feature extraction algorithms
CSqNeighbourhood	Square neighborhood structure
▼Nvis
CCCameraControl	Trackball camera control class
CCMarker	Marker class
CCMarkerHistogram	Histogram Marker class
CCTrackballCamera	Trackball camera class
CCBaseRandomModel	Base abstract class for random model training
CCCMat	Confusion matrix class
CCDecode	Base abstract class for random model decoding
CCDecodeExact	Exact decoding class
CCDiffFeaturesConcatenator	Difference features concatenator class
CCFeaturesConcatenator	Features concatenator base abstract class
CCGraph	Pairwise graph class
CCGraph3	Triple graph class
CCGraphBoost
CCGraphExt	Extended Pairwise graph class
CCGraphLayered	Extended Pairwise Layered graph class
▼CCGraphWeiss	Pairwise graph class
CEdge	Edge structure
CNode	Node structure
CCInfer	Base abstract class for random model inference
CCInferChain	Inference for chain graphs
CCInferExact	Exact inference class
CCInferLBP	Sum product Loopy Belief Propagation inference class
CCInferTree	Inference for tree graphs (undirected graphs without loops)
CCInferTRW	Tree-reweighted inference class
CCInferViterbi	Max product Viterbi inference class
CCMessagePassing	Abstract base class for message passing inference algorithmes
CCNDGauss	Multi - dimensional Gauss function class
CCPDFGaussian	Gaissian-based PDF class
CCPDFHistogram	Histogram-based PDF class
CCPowell	The Powell search method class
CCPrior	Base abstract class for prior probability estimation
CCPriorEdge	Edge prior probability estimation class
CCPriorNode	Node prior probability estimation class
CCPriorTriplet	Triplet prior probability estimation class
CCRForest	Random Forest class
CCSimpleFeaturesConcatenator	Simple features concatenator class
CCTrainEdge	Base abstract class for edge potentials training
CCTrainEdgeConcat	Concatenated edge training class
CCTrainEdgePotts	Potts edge training class
CCTrainEdgePottsCS	Contrast-Sensitive Potts training class
CCTrainEdgePrior	Contrast-Sensitive Potts training with edge prior probability class
CCTrainLink	Base abstract class for link (inter-layer edge) potentials training
CCTrainLinkNested	Nested link (inter-layer edge) training class
CCTrainNode	Base abstract class for node potentials training
CCTrainNodeCvGM	OpenCV Gaussian Model training class
CCTrainNodeCvGMM	OpenCV Gaussian Mixture Model training class
CCTrainNodeCvRF	OpenCV Random Forest training class
CCTrainNodeGM	Gaussian Model training class
CCTrainNodeGMM	Gaussian Mixture Model training class
CCTrainNodeMsRF	Microsoft Sherwood Random Forest training class
CCTrainNodeNaiveBayes	Naive Bayes training class
CCTrainTriplet	Base abstract class for triplet potential training
CEdge	Edge structure
CIGraph	Interface class for graphical models
CIPDF	Interface class for Probability Density Function (PDF)
CITrain	Interface class for random model training
CNode	Node structure
CTrainNodeCvGMMParams	OpenCV Random Forest parameters
CTrainNodeCvRFParams	OpenCV Random Forest parameters
CTrainNodeGMMParams	Gaussian Mixture Model parameters
CTrainNodeMsRFParams	Microsoft Research Random Forest parameters
CTriplet	Triplet structure
▼NMicrosoftResearch
▼NCambridge
▼NSherwood
CForest