Namespaces
	fex

	mathop
	Mathematical operations.

	parallel

	random
	Random number generation.

	Serialize
	OpenCV Mat Serialization class.

	Timer
	Timer

	vis

Classes
class	CBaseRandomModel
	Base abstract class for random model training. More...

class	CCMat
	Confusion matrix class. More...

class	CDecode
	Base abstract class for random model decoding. More...

class	CDecodeExact
	Exact decoding class. More...

class	CDiffFeaturesConcatenator
	Difference features concatenator class. More...

class	CEdgeModelPotts
	Potts Edge Model for dense graphical models. More...

class	CFeaturesConcatenator
	Features concatenator base abstract class. More...

class	CGraph
	Interface class for graphical models. More...

class	CGraph3
	Triple graph class. More...

class	CGraphDense
	Fully-connected (dense) graph class. More...

class	CGraphDenseExt
	Extended Dense graph class for 2D image classifaction. More...

class	CGraphDenseKit
	Kit class for constructing Dense Graph objects. More...

class	CGraphExt
	General graph extension abstract class for 2D image classifaction. More...

class	CGraphKit
	Abstract Kit class for constructing Graph-related objects. More...

class	CGraphLayeredExt
	Extended Pairwise Layered graph class. More...

class	CGraphPairwise
	Pairwise graph class. More...

class	CGraphPairwiseExt
	Extended Pairwise graph class for 2D image classifaction. More...

class	CGraphPairwiseKit
	Kit class for constructing Pairwise Graph objects. More...

class	CGraphWeiss
	Pairwise graph class. More...

class	CInfer
	Base abstract class for random model inference. More...

class	CInferChain
	Inference for chain graphs. More...

class	CInferDense
	Dense Inference for Dense CRF. More...

class	CInferExact
	Exact inference class. More...

class	CInferLBP
	Sum product Loopy Belief Propagation inference class. More...

class	CInferTree
	Inference for tree graphs (undirected graphs without loops) More...

class	CInferTRW
	Tree-reweighted inference class. More...

class	CInferViterbi
	Max product Viterbi inference class. More...

class	CKDGauss
	Multivariate Gaussian distribution class. More...

class	CKDNode
	k-D Node class for the k-D Tree data structure More...

class	CKDTree
	Class implementing k-D Tree data structure. More...

class	CMessagePassing
	Abstract base class for message passing inference algorithmes. More...

class	CPDFGaussian
	Gaissian-based PDF class. More...

class	CPDFHistogram
	Histogram-based PDF class (1D) More...

class	CPDFHistogram2D
	Histogram-based PDF class (2D) More...

class	CPowell
	The Powell search method class. More...

class	CPrior
	Base abstract class for prior probability estimation. More...

class	CPriorEdge
	Edge prior probability estimation class. More...

class	CPriorNode
	Node prior probability estimation class More...

class	CPriorTriplet
	Triplet prior probability estimation class. More...

class	CSamplesAccumulator
	Samples accumulator abstract class. More...

class	CSimpleFeaturesConcatenator
	Simple features concatenator class. More...

class	CTrainEdge
	Base abstract class for edge potentials training. More...

class	CTrainEdgeConcat
	Concatenated edge training class. More...

class	CTrainEdgePotts
	Potts edge training class. More...

class	CTrainEdgePottsCS
	Contrast-Sensitive Potts training class. More...

class	CTrainEdgePrior
	Contrast-Sensitive Potts training with edge prior probability class. More...

class	CTrainLink
	Base abstract class for link (inter-layer edge) potentials training. More...

class	CTrainLinkNested
	Nested link (inter-layer edge) training class. More...

class	CTrainNode
	Base abstract class for node potentials training. More...

class	CTrainNodeBayes
	Bayes training class. More...

class	CTrainNodeCvANN
	OpenCV Artificial neural network training class. More...

class	CTrainNodeCvGM
	OpenCV Gaussian Model training class. More...

class	CTrainNodeCvGMM
	OpenCV Gaussian Mixture Model training class. More...

class	CTrainNodeCvKNN
	OpenCV Nearest Neighbor training class. More...

class	CTrainNodeCvRF
	OpenCV Random Forest training class. More...

class	CTrainNodeCvSVM
	OpenCV Support Vector Machines training class. More...

class	CTrainNodeGM
	Gaussian Model training class. More...

class	CTrainNodeGMM
	Gaussian Mixture Model training class. More...

class	CTrainNodeKNN
	Nearest Neighbor training class. More...

class	CTrainNodeMsRF
	Microsoft Sherwood Random Forest training class. More...

class	CTrainTriplet
	Base abstract class for triplet potential training. More...

struct	Edge
	Edge structure More...

class	IEdgeModel
	Interface class for edge models used in dense graphical models. More...

class	IGraphPairwise
	Interface class for graphical models. More...

class	IPDF
	Interface class for Probability Density Function (PDF) More...

class	ITrain
	Interface class for random model training. More...

struct	Node
	Node structure More...

struct	TrainNodeCvANNParams
	OpenCV Artificial neural network parameters. More...

struct	TrainNodeCvGMMParams
	OpenCV Random Forest parameters. More...

struct	TrainNodeCvKNNParams
	OpenCV k-Nearest Neighbors parameters. More...

struct	TrainNodeCvRFParams
	OpenCV Random Forest parameters. More...

struct	TrainNodeCvSVMParams
	OpenCV Support Vector machine parameters. More...

struct	TrainNodeGMMParams
	Gaussian Mixture Model parameters. More...

struct	TrainNodeKNNParams
	k-Nearest Neighbors parameters More...

struct	TrainNodeMsRFParams
	Microsoft Research Random Forest parameters. More...

struct	Triplet
	Triplet structure More...

Typedefs
using	vec_triplet_t = std::vector< Triplet >

using	ptr_node_t = std::unique_ptr< Node >

using	vec_node_t = std::vector< ptr_node_t >

using	ptr_edge_t = std::unique_ptr< Edge >

using	vec_edge_t = std::vector< ptr_edge_t >

using	GaussianMixture = std::vector< CKDGauss >

typedef struct DirectGraphicalModels::TrainNodeCvANNParams	TrainNodeCvANNParams
	OpenCV Artificial neural network parameters. More...

typedef struct DirectGraphicalModels::TrainNodeCvGMMParams	TrainNodeCvGMMParams
	OpenCV Random Forest parameters. More...

typedef struct DirectGraphicalModels::TrainNodeCvKNNParams	TrainNodeCvKNNParams
	OpenCV k-Nearest Neighbors parameters. More...

typedef struct DirectGraphicalModels::TrainNodeCvRFParams	TrainNodeCvRFParams
	OpenCV Random Forest parameters. More...

typedef struct DirectGraphicalModels::TrainNodeCvSVMParams	TrainNodeCvSVMParams
	OpenCV Support Vector machine parameters. More...

typedef struct DirectGraphicalModels::TrainNodeGMMParams	TrainNodeGMMParams
	Gaussian Mixture Model parameters. More...

typedef struct DirectGraphicalModels::TrainNodeKNNParams	TrainNodeKNNParams
	k-Nearest Neighbors parameters More...

typedef struct DirectGraphicalModels::TrainNodeMsRFParams	TrainNodeMsRFParams
	Microsoft Research Random Forest parameters. More...

using	vec_nColor_t = std::vector< std::pair< Scalar, std::string > >

using	ptr_edgeModel_t = std::shared_ptr< IEdgeModel >

Enumerations
enum	RandomModelType { RM_UNARY = 1, RM_PAIRWISE, RM_TRIPLET }
	Random model types. More...

enum	GraphType { GraphType::pairwise, GraphType::dense }
	Types of the graphical model. More...

enum	graphEdgesType { GRAPH_EDGES_NONE = 0, GRAPH_EDGES_GRID = 1, GRAPH_EDGES_DIAG = 2, GRAPH_EDGES_LINK = 4 }
	Graph edges types. More...

enum	INFER { INFER::LBP, INFER::TRW, INFER::Viterbi }
	Types of the inference / decoding objects. More...

enum	ePotNormApproach { eP_APP_NORM_STANDARD, eP_APP_NORM_SYMMETRIC, eP_APP_NORM_ASYMMETRIC }
	Normalization approach flag. More...

enum	EdgeRandomModel : byte { Potts = 0, PottsCS, Prior, Concat }
	Types of the edge potential finction. More...

enum	ePotPenalApproach { eP_APP_PEN_CHAR, eP_APP_PEN_PM, eP_APP_PEN_EXP }
	Penalization approach flag. More...

enum	NodeRandomModel : byte { Bayes = 0, GMM, CvGMM, KNN, CvKNN, CvRF, MsRF, CvANN, CvSVM, GM, CvGM }
	Types of the node potential finction. More...

Functions
float	getAveragePrecision (const vec_byte_t &predictions, const vec_float_t &potentials, const vec_byte_t &gt, byte state)
	Returns Average Precision for the selected state (class) state. More...

float	calculateContrast (const Mat &featureVector1, const Mat &featureVector2)

float	penalizerChar (float x, float l)

float	penalizerPM (float x, float l)

float	penalizerExp (float x, float l)

double	fastPow (double a, double b)

template<typename T , void(T::*)(byte b) SomeMethod>
void	DGM_ELEMENTWISE1 (T &self, const Mat &m)

template<typename T , void(T::*)(byte b1, byte b2) SomeMethod>
void	DGM_ELEMENTWISE2 (T &self, const Mat &m1, const Mat &m2)

template<typename T , void(T::*)(byte b1, byte b2) SomeMethod>
void	DGM_ELEMENTWISE2 (T &self, const Mat &m1, const Mat &m2, const Mat &mask)

template<typename T , void(T::*)(const Mat &vec, byte b) SomeMethod>
void	DGM_VECTORWISE1 (T &self, const Mat &m1, const Mat &m2)

template<typename T , void(T::*)(const Mat &vec, byte b) SomeMethod>
void	DGM_VECTORWISE1 (T &self, const vec_mat_t &m1, const Mat &m2)

template<typename T , void(T::*)(const Mat &vec, byte b1, byte b2) SomeMethod>
void	DGM_VECTORWISE2 (T &self, const Mat &m1, const Mat &m2, const Mat &m3)

template<typename T , void(T::*)(const Mat &vec, byte b1, byte b2) SomeMethod>
void	DGM_VECTORWISE2 (T &self, const vec_mat_t &m1, const Mat &m2, const Mat &m3)

Variables
const TrainNodeCvANNParams	TRAIN_NODE_CV_ANN_PARAMS_DEFAULT

const TrainNodeCvGMMParams	TRAIN_NODE_CV_GMM_PARAMS_DEFAULT

const TrainNodeCvKNNParams	TRAIN_NODE_CV_KNN_PARAMS_DEFAULT

const TrainNodeCvRFParams	TRAIN_NODE_CV_RF_PARAMS_DEFAULT

const TrainNodeCvSVMParams	TRAIN_NODE_CV_SVM_PARAMS_DEFAULT

const TrainNodeGMMParams	TRAIN_NODE_GMM_PARAMS_DEFAULT

const TrainNodeKNNParams	TRAIN_NODE_KNN_PARAMS_DEFAULT

const TrainNodeMsRFParams	TRAIN_NODE_MS_RF_PARAMS_DEFAULT

const size_t	STR_LEN = 256

Typedef Documentation

◆ GaussianMixture

using DirectGraphicalModels::GaussianMixture = typedef std::vector<CKDGauss>

Definition at line 230 of file KDGauss.h.

◆ ptr_edge_t

using DirectGraphicalModels::ptr_edge_t = typedef std::unique_ptr<Edge>

Definition at line 55 of file GraphPairwise.h.

◆ ptr_edgeModel_t

using DirectGraphicalModels::ptr_edgeModel_t = typedef std::shared_ptr<IEdgeModel>

Definition at line 74 of file types.h.

◆ ptr_node_t

using DirectGraphicalModels::ptr_node_t = typedef std::unique_ptr<Node>

Definition at line 24 of file GraphPairwise.h.

◆ TrainNodeCvANNParams

typedef struct DirectGraphicalModels::TrainNodeCvANNParams DirectGraphicalModels::TrainNodeCvANNParams

OpenCV Artificial neural network parameters.

◆ TrainNodeCvGMMParams

typedef struct DirectGraphicalModels::TrainNodeCvGMMParams DirectGraphicalModels::TrainNodeCvGMMParams

OpenCV Random Forest parameters.

◆ TrainNodeCvKNNParams

typedef struct DirectGraphicalModels::TrainNodeCvKNNParams DirectGraphicalModels::TrainNodeCvKNNParams

OpenCV k-Nearest Neighbors parameters.

◆ TrainNodeCvRFParams

typedef struct DirectGraphicalModels::TrainNodeCvRFParams DirectGraphicalModels::TrainNodeCvRFParams

OpenCV Random Forest parameters.

◆ TrainNodeCvSVMParams

typedef struct DirectGraphicalModels::TrainNodeCvSVMParams DirectGraphicalModels::TrainNodeCvSVMParams

OpenCV Support Vector machine parameters.

◆ TrainNodeGMMParams

typedef struct DirectGraphicalModels::TrainNodeGMMParams DirectGraphicalModels::TrainNodeGMMParams

Gaussian Mixture Model parameters.

◆ TrainNodeKNNParams

typedef struct DirectGraphicalModels::TrainNodeKNNParams DirectGraphicalModels::TrainNodeKNNParams

k-Nearest Neighbors parameters

◆ TrainNodeMsRFParams

typedef struct DirectGraphicalModels::TrainNodeMsRFParams DirectGraphicalModels::TrainNodeMsRFParams

Microsoft Research Random Forest parameters.

◆ vec_edge_t

using DirectGraphicalModels::vec_edge_t = typedef std::vector<ptr_edge_t>

Definition at line 56 of file GraphPairwise.h.

◆ vec_nColor_t

using DirectGraphicalModels::vec_nColor_t = typedef std::vector<std::pair<Scalar, std::string> >

Definition at line 73 of file types.h.

◆ vec_node_t

using DirectGraphicalModels::vec_node_t = typedef std::vector<ptr_node_t>

Definition at line 25 of file GraphPairwise.h.

◆ vec_triplet_t

using DirectGraphicalModels::vec_triplet_t = typedef std::vector<Triplet>

Definition at line 23 of file Graph3.h.

Enumeration Type Documentation

◆ EdgeRandomModel

enum DirectGraphicalModels::EdgeRandomModel : byte

Types of the edge potential finction.

Enumerator
Potts	Potts Model.
PottsCS	Contrast-Sensitive Potts Model.
Prior	Contrast-Sensitive Potts Model with Prior.
Concat	Concatenated Model.

Definition at line 10 of file TrainEdge.h.

◆ ePotNormApproach

enum DirectGraphicalModels::ePotNormApproach

Normalization approach flag.

These flags specify the approach for normalization of the edge potential matrix. The difference between symmetric and asymmetric approaches:

Standard approach performs a per-element multiplication of the histogram matrix with the calar value \(1/sum\), where \(sum\) - is the sum of all matrix elements.
Let \(A,B\in\mathbb{S}\) - are any two different states (classes) and \(N_{AA}\) (and, consequently \(N_{BB}\)) - are number of within class transitions in training data, i.e. the doubled number of neighboring sites with the same state (class). And \(N_{AB}\equiv N_{BA}\) - are the number of transitions between classes \(A\) and \(B\), i.e. the number of neighboring sites with states (classes) \(A\) and \(B\).
Symmetric approach after the normalization guaranties that resulting ePot matrix is symmetric, i.e. \(M_{AB} = M_{BA}\) and \(M_{AA} = M_{BB} = 1\). In case of \(N_{AA}, N_{BB} > N_{AB}\equiv N_{BA}\), the largest number in ePot matrix will be \(1\equiv M_{AA}\equiv M_{BB}\) (ones on the diagonal); and in case of \(N_{AB}\cdot N_{AB}\geq N_{AA}\cdot N_{BB}\), it is possible to have values larger than \(M_{AB} > 1\). This case is possible to model for academic purpose, but it is almost improbable in real life applications.
Asymmetric approach takes into account the difference in number of occurrences of states (classes) in the training data (i.e. prior probabilities \(p(A)\) and \(p(B)\)). After the normalization it guaranties that the largest value in ePot matrix does not exceed \(1\). The case, when the diagonal values of the matrix are not the largest is usually probable (e.g. \(1 = AB > AA\) after the normalization). This may lead to erosion of areas with small occurrence rate.
The result of asymmetric approach could be approximated from the result of symmetric approach, by multiplying the columns of the ePot matrix with the corresponding to the base columns state (class) the prior state (class) probability.

Enumerator
eP_APP_NORM_STANDARD	Standard approach.
eP_APP_NORM_SYMMETRIC	Symmetric approach.
eP_APP_NORM_ASYMMETRIC	Asymmetric approach.

Definition at line 27 of file PriorEdge.h.

◆ ePotPenalApproach

enum DirectGraphicalModels::ePotPenalApproach

Penalization approach flag.

These flags specify which penalization function \(\mathcal{P}(d;\,\lambda)\) for penalizing the diagonal elements of the edge potential matrix will be used. Argument \(d\) usually represent the Euclidean distance between two feature vectors, which correspond to the both nodes of an edge, and thus, is a measure of similarity (or contrast) between them. Parameter \(\lambda\) defines the penalization strength. The penaliztion functions are given as follows:

Charbonnier penalization approach: \(\mathcal{P}(d;\,\lambda) = \frac{\lambda}{\sqrt{\lambda^2 + d^2}}\)
Perrona-Malik penalization approach: \(\mathcal{P}(d;\,\lambda) = \frac{\lambda^2}{\lambda^2 + d^2}\)
Exponential penalization approach: \(\mathcal{P}(d;\,\lambda) = e^{-\lambda d^2}\)

Enumerator
eP_APP_PEN_CHAR	Charbonnier penalization approach.
eP_APP_PEN_PM	Perrona-Malik penalization approach.
eP_APP_PEN_EXP	Exponential penalization approach.

Definition at line 18 of file TrainEdgePottsCS.h.

◆ graphEdgesType

enum DirectGraphicalModels::graphEdgesType

Graph edges types.

Define the grid of edges, connecting nodes of the graph

Enumerator
GRAPH_EDGES_NONE	No edges.
GRAPH_EDGES_GRID	Vertical and horizontal edges.
GRAPH_EDGES_DIAG	Diagonal edges.
GRAPH_EDGES_LINK	Links (inter-layer edges)

Definition at line 18 of file GraphLayeredExt.h.

◆ GraphType

enum DirectGraphicalModels::GraphType

strong

Types of the graphical model.

Enumerator
pairwise	Pairwise graph.
dense	Dense (complete) graph.

Definition at line 10 of file GraphKit.h.

◆ INFER

enum DirectGraphicalModels::INFER

strong

Types of the inference / decoding objects.

Enumerator
LBP	Loopy Belief Propagation inference.
TRW	Convergent Tree-Reweighted inference.
Viterbi	Viterbi inference.

Definition at line 21 of file GraphPairwiseKit.h.

◆ NodeRandomModel

enum DirectGraphicalModels::NodeRandomModel : byte

Types of the node potential finction.

Enumerator
Bayes	Bayes Model.
GMM	Gaussian Mixture Model.
CvGMM	OpenCV Gaussian Mixture Model.
KNN	Nearest Neighbor.
CvKNN	OpenCV Nearest Neighbor.
CvRF	OpenCV Random Forest.
MsRF	MicroSoft Random Forest.
CvANN	OpenCV Artificial Neural Network.
CvSVM	OpenCV Support Vector Machines.
GM	Gaussian Model.
CvGM	OpenCV Gaussian Model.

Definition at line 10 of file TrainNode.h.

◆ RandomModelType

enum DirectGraphicalModels::RandomModelType

Random model types.

Define the maximal number of nodes in the cliques

Enumerator
RM_UNARY	Unary random model: no iteraction between nodes.
RM_PAIRWISE	Pairwise random model: maximum two nodes in the cliques.
RM_TRIPLET	Triplet random model: maximum tree nodes in the cliques.

Definition at line 13 of file BaseRandomModel.h.

Function Documentation

◆ calculateContrast()

float DirectGraphicalModels::calculateContrast	(	const Mat &	featureVector1,
		const Mat &	featureVector2
	)

Definition at line 7 of file TrainEdgePottsCS.cpp.

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◆ DGM_ELEMENTWISE1()

template<typename T , void(T::*)(byte b) SomeMethod>

void DirectGraphicalModels::DGM_ELEMENTWISE1	(	T &	self,
		const Mat &	m
	)

inline

Definition at line 61 of file macroses.h.

◆ DGM_ELEMENTWISE2() [1/2]

template<typename T , void(T::*)(byte b1, byte b2) SomeMethod>

void DirectGraphicalModels::DGM_ELEMENTWISE2	(	T &	self,
		const Mat &	m1,
		const Mat &	m2
	)

inline

Definition at line 75 of file macroses.h.

◆ DGM_ELEMENTWISE2() [2/2]

template<typename T , void(T::*)(byte b1, byte b2) SomeMethod>

void DirectGraphicalModels::DGM_ELEMENTWISE2	(	T &	self,
		const Mat &	m1,
		const Mat &	m2,
		const Mat &	mask
	)

inline

Definition at line 91 of file macroses.h.

◆ DGM_VECTORWISE1() [1/2]

template<typename T , void(T::*)(const Mat &vec, byte b) SomeMethod>

void DirectGraphicalModels::DGM_VECTORWISE1	(	T &	self,
		const Mat &	m1,
		const Mat &	m2
	)

inline

Definition at line 109 of file macroses.h.

◆ DGM_VECTORWISE1() [2/2]

template<typename T , void(T::*)(const Mat &vec, byte b) SomeMethod>

void DirectGraphicalModels::DGM_VECTORWISE1	(	T &	self,
		const vec_mat_t &	m1,
		const Mat &	m2
	)

inline

Definition at line 128 of file macroses.h.

◆ DGM_VECTORWISE2() [1/2]

template<typename T , void(T::*)(const Mat &vec, byte b1, byte b2) SomeMethod>

void DirectGraphicalModels::DGM_VECTORWISE2	(	T &	self,
		const Mat &	m1,
		const Mat &	m2,
		const Mat &	m3
	)

inline

Definition at line 150 of file macroses.h.

◆ DGM_VECTORWISE2() [2/2]

template<typename T , void(T::*)(const Mat &vec, byte b1, byte b2) SomeMethod>

void DirectGraphicalModels::DGM_VECTORWISE2	(	T &	self,
		const vec_mat_t &	m1,
		const Mat &	m2,
		const Mat &	m3
	)

inline

Definition at line 172 of file macroses.h.

◆ fastPow()

double DirectGraphicalModels::fastPow	(	double	a,
		double	b
	)

inline

Definition at line 45 of file macroses.h.

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◆ penalizerChar()

float DirectGraphicalModels::penalizerChar	(	float	x,
		float	l
	)

Definition at line 31 of file TrainEdgePottsCS.cpp.

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◆ penalizerExp()

float DirectGraphicalModels::penalizerExp	(	float	x,
		float	l
	)

Definition at line 45 of file TrainEdgePottsCS.cpp.

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◆ penalizerPM()

float DirectGraphicalModels::penalizerPM	(	float	x,
		float	l
	)

Definition at line 38 of file TrainEdgePottsCS.cpp.

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Variable Documentation

◆ STR_LEN

const size_t DirectGraphicalModels::STR_LEN = 256

Definition at line 75 of file types.h.

◆ TRAIN_NODE_CV_ANN_PARAMS_DEFAULT

const TrainNodeCvANNParams DirectGraphicalModels::TRAIN_NODE_CV_ANN_PARAMS_DEFAULT

Initial value:

=   TrainNodeCvANNParams(
                                                                    5,      
                                                                    0.0001, 
                                                                    0.1,    
                                                                    100,    
                                                                    0.01,   
                                                                    TermCriteria::MAX_ITER | TermCriteria::EPS, 
                                                                    0       
                                                                    )

Definition at line 25 of file TrainNodeCvANN.h.

◆ TRAIN_NODE_CV_GMM_PARAMS_DEFAULT

const TrainNodeCvGMMParams DirectGraphicalModels::TRAIN_NODE_CV_GMM_PARAMS_DEFAULT

Initial value:

= TrainNodeCvGMMParams(
                                                                16,                                         
                                                                ml::EM::COV_MAT_DIAGONAL,                   
                                                                100,                                        
                                                                0.01,                                       
                                                                TermCriteria::MAX_ITER | TermCriteria::EPS, 
                                                                0                                           
                                                                )

Definition at line 24 of file TrainNodeCvGMM.h.

◆ TRAIN_NODE_CV_KNN_PARAMS_DEFAULT

const TrainNodeCvKNNParams DirectGraphicalModels::TRAIN_NODE_CV_KNN_PARAMS_DEFAULT

Initial value:

=   TrainNodeCvKNNParams(
                                                                    0.1f,   
                                                                    100,    
                                                                    0       
                                                                    )

Definition at line 21 of file TrainNodeCvKNN.h.

◆ TRAIN_NODE_CV_RF_PARAMS_DEFAULT

const TrainNodeCvRFParams DirectGraphicalModels::TRAIN_NODE_CV_RF_PARAMS_DEFAULT

Initial value:

= TrainNodeCvRFParams(
                                                                25,     
                                                                5,      
                                                                0,      
                                                                false,  
                                                                15,     
                                                                false,  
                                                                4,      
                                                                100,    
                                                                0.01,   
                                                                TermCriteria::MAX_ITER | TermCriteria::EPS, 
                                                                0       
                                                                )

Definition at line 30 of file TrainNodeCvRF.h.

◆ TRAIN_NODE_CV_SVM_PARAMS_DEFAULT

const TrainNodeCvSVMParams DirectGraphicalModels::TRAIN_NODE_CV_SVM_PARAMS_DEFAULT

Initial value:

=   TrainNodeCvSVMParams(
                                                                    0.4,    
                                                                    10000,  
                                                                    0.01,   
                                                                    TermCriteria::MAX_ITER | TermCriteria::EPS, 
                                                                    0       
                                                                    )

Definition at line 23 of file TrainNodeCvSVM.h.

◆ TRAIN_NODE_GMM_PARAMS_DEFAULT

const TrainNodeGMMParams DirectGraphicalModels::TRAIN_NODE_GMM_PARAMS_DEFAULT

Initial value:

= TrainNodeGMMParams(
        64,     
        64,     
        64,     
        -16,    
        -16     
    )

Definition at line 23 of file TrainNodeGMM.h.

◆ TRAIN_NODE_KNN_PARAMS_DEFAULT

const TrainNodeKNNParams DirectGraphicalModels::TRAIN_NODE_KNN_PARAMS_DEFAULT

Initial value:

=   TrainNodeKNNParams(
                                                                0.1f,   
                                                                100,    
                                                                0       
                                                                )

Definition at line 22 of file TrainNodeKNN.h.

◆ TRAIN_NODE_MS_RF_PARAMS_DEFAULT

const TrainNodeMsRFParams DirectGraphicalModels::TRAIN_NODE_MS_RF_PARAMS_DEFAULT

Initial value:

= TrainNodeMsRFParams(
                                                                10,     
                                                                10,     
                                                                10,     
                                                                10,     
                                                                false,  
                                                                0       
                                                                )

Definition at line 35 of file TrainNodeMsRF.h.

Namespaces

Classes

Typedefs

Enumerations

Functions

Variables

Typedef Documentation

◆ GaussianMixture

◆ ptr_edge_t

◆ ptr_edgeModel_t

◆ ptr_node_t

◆ TrainNodeCvANNParams

◆ TrainNodeCvGMMParams

◆ TrainNodeCvKNNParams

◆ TrainNodeCvRFParams

◆ TrainNodeCvSVMParams

◆ TrainNodeGMMParams

◆ TrainNodeKNNParams

◆ TrainNodeMsRFParams

◆ vec_edge_t

◆ vec_nColor_t

◆ vec_node_t

◆ vec_triplet_t

Enumeration Type Documentation

◆ EdgeRandomModel

◆ ePotNormApproach

◆ ePotPenalApproach

◆ graphEdgesType

◆ GraphType

◆ INFER

◆ NodeRandomModel

◆ RandomModelType

Function Documentation

◆ calculateContrast()

◆ DGM_ELEMENTWISE1()

◆ DGM_ELEMENTWISE2() [1/2]

◆ DGM_ELEMENTWISE2() [2/2]

◆ DGM_VECTORWISE1() [1/2]

◆ DGM_VECTORWISE1() [2/2]

◆ DGM_VECTORWISE2() [1/2]

◆ DGM_VECTORWISE2() [2/2]

◆ fastPow()

◆ penalizerChar()

◆ penalizerExp()

◆ penalizerPM()

Variable Documentation

◆ STR_LEN

◆ TRAIN_NODE_CV_ANN_PARAMS_DEFAULT

◆ TRAIN_NODE_CV_GMM_PARAMS_DEFAULT

◆ TRAIN_NODE_CV_KNN_PARAMS_DEFAULT

◆ TRAIN_NODE_CV_RF_PARAMS_DEFAULT

◆ TRAIN_NODE_CV_SVM_PARAMS_DEFAULT

◆ TRAIN_NODE_GMM_PARAMS_DEFAULT

◆ TRAIN_NODE_KNN_PARAMS_DEFAULT

◆ TRAIN_NODE_MS_RF_PARAMS_DEFAULT