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
CCEdgeModelPotts	Potts Edge Model for dense graphical models
CCFeaturesConcatenator	Features concatenator base abstract class
CCGraph	Interface class for graphical models
CCGraph3	Triple graph class
CCGraphDense	Fully-connected (dense) graph class
CCGraphDenseExt	Extended Dense graph class for 2D image classifaction
CCGraphDenseKit	Kit class for constructing Dense Graph objects
CCGraphExt	General graph extension abstract class for 2D image classifaction
CCGraphKit	Abstract Kit class for constructing Graph-related objects
CCGraphLayeredExt	Extended Pairwise Layered graph class
CCGraphPairwise	Pairwise graph class
CCGraphPairwiseExt	Extended Pairwise graph class for 2D image classifaction
CCGraphPairwiseKit	Kit class for constructing Pairwise Graph objects
▶CCGraphWeiss	Pairwise graph class
CEdge	Edge structure
CNode	Node structure
CCInfer	Base abstract class for random model inference
CCInferChain	Inference for chain graphs
CCInferDense	Dense Inference for Dense CRF
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
CCKDGauss	Multivariate Gaussian distribution class
CCKDNode	K-D Node class for the k-D Tree data structure
CCKDTree	Class implementing k-D Tree data structure
CCMessagePassing	Abstract base class for message passing inference algorithmes
CCPDFGaussian	Gaissian-based PDF class
CCPDFHistogram	Histogram-based PDF class (1D)
CCPDFHistogram2D	Histogram-based PDF class (2D)
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
CCSamplesAccumulator	Samples accumulator abstract 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
CCTrainNodeBayes	Bayes training class
CCTrainNodeCvANN	OpenCV Artificial neural network training class
CCTrainNodeCvGM	OpenCV Gaussian Model training class
CCTrainNodeCvGMM	OpenCV Gaussian Mixture Model training class
CCTrainNodeCvKNN	OpenCV Nearest Neighbor training class
CCTrainNodeCvRF	OpenCV Random Forest training class
CCTrainNodeCvSVM	OpenCV Support Vector Machines training class
CCTrainNodeGM	Gaussian Model training class
CCTrainNodeGMM	Gaussian Mixture Model training class
CCTrainNodeKNN	Nearest Neighbor training class
CCTrainNodeMsRF	Microsoft Sherwood Random Forest training class
CCTrainTriplet	Base abstract class for triplet potential training
CEdge	Edge structure
CIEdgeModel	Interface class for edge models used in dense graphical models
CIGraphPairwise	Interface class for graphical models
CIPDF	Interface class for Probability Density Function (PDF)
CITrain	Interface class for random model training
CNode	Node structure
CTrainNodeCvANNParams	OpenCV Artificial neural network parameters
CTrainNodeCvGMMParams	OpenCV Random Forest parameters
CTrainNodeCvKNNParams	OpenCV k-Nearest Neighbors parameters
CTrainNodeCvRFParams	OpenCV Random Forest parameters
CTrainNodeCvSVMParams	OpenCV Support Vector machine parameters
CTrainNodeGMMParams	Gaussian Mixture Model parameters
CTrainNodeKNNParams	K-Nearest Neighbors parameters
CTrainNodeMsRFParams	Microsoft Research Random Forest parameters
CTriplet	Triplet structure
▼NMicrosoftResearch
▶NCambridge
▶NSherwood
CForest