Direct Graphical Models
v.1.5.2

Namespaces  
global  
Globalfeatures extraction.  
Classes  
class  CCommonFeatureExtractor 
Common class, which unites feature extraction algorithms. More...  
class  CCoordinate 
Coordinate feature extraction class. More...  
class  CDistance 
Distance feature extraction class. More...  
class  CGlobalFeatureExtractor 
Interface class for global feature extraction algorithms. More...  
class  CGradient 
Gradient feature extraction class. More...  
class  CHOG 
HOG (histogram of oriented gradients) feature extraction class. More...  
class  CHSV 
Hue, Saturation and Value feature extraction class. More...  
class  CIntensity 
Intensity feature extraction class. More...  
class  CNDVI 
NDVI (normalized difference vegetation index) feature extraction class. More...  
class  CScale 
Scale feature extraction class. More...  
class  CSIFT 
SIFT (scaleinvariant feature transform) feature extraction class. More...  
class  CSparseCoding 
Sparse Coding feature extraction class. More...  
class  CSparseDictionary 
Sparse Dictionary Learning class. More...  
class  CVariance 
Variance feature extraction class. More...  
class  IFeatureExtractor 
Interface class for feature extraction algorithms. More...  
class  ILocalFeatureExtractor 
Interface class for local feature extraction algorithms. More...  
struct  SqNeighbourhood 
Square neighborhood structure. More...  
Typedefs  
typedef struct DirectGraphicalModels::fex::SqNeighbourhood  SqNeighbourhood 
Square neighborhood structure. More...  
Enumerations  
enum  ChannelsRGB { CH_BLUE, CH_GREEN, CH_RED } 
Channels in the BGR color space. More...  
enum  ChannelsHSV { CH_HUE, CH_SATURATION, CH_VALUE } 
Channels in the HSV color space. More...  
enum  coordinateType { COORDINATE_ORDINATE, COORDINATE_ABSCISS, COORDINATE_RADIUS } 
Types of the coordinate feature. More...  
enum  BasePointLocation { BP_CENTER, BP_LEFT, BP_RIGHT, BP_TOP, BP_BOTTOM } 
Some special cases of the base point location inside the neighborhood. More...  
Functions  
byte  linear_mapper (float val, float min, float max) 
Linear 1D mapping. More...  
byte  two_linear_mapper (float val, float min, float max, float mid, byte midPoint) 
Twolinear 1D mapping. More...  
SqNeighbourhood  sqNeighbourhood (int leftGap, int rightGap, int upperGap, int lowerGap) 
Initializes the square neighborhood structure. More...  
SqNeighbourhood  sqNeighbourhoodAll (int R) 
Initializes the square neighborhood structure with all the same values (base point in the center) More...  
SqNeighbourhood  sqNeighbourhood (int R, BasePointLocation location=BP_CENTER) 
Initializes the square neighborhood structure with a predefine shape. More...  
Variables  
const float  SC_LRATE_W = 5e2f 
Learning rate (speed) for weights \(W\). More...  
const float  SC_LRATE_D = 1e2f 
Learning rate (speed) for dictionary \(D\). More...  
typedef struct DirectGraphicalModels::fex::SqNeighbourhood DirectGraphicalModels::fex::SqNeighbourhood 
Square neighborhood structure.
This structure defines rectangular neighborhood around its base point. The size of the neighborhoood is given via four gap values: \((leftGap+rightGap+1)\times(upperGap+lowerGap+1)\), and its shape is defines as depicted at Figure 1.
This definition of a neighbourhood extends the classical one, where the neighborhood is represented as a square with the base point in the center.
Some special cases of the base point location inside the neighborhood.
Definition at line 26 of file SquareNeighborhood.h.
Channels in the HSV color space.
Enumerator  

CH_HUE 
Hue channel. 
CH_SATURATION 
Saturation channel. 
CH_VALUE 
Value channel. 
Definition at line 30 of file CommonFeatureExtractor.h.
Channels in the BGR color space.
Enumerator  

CH_BLUE 
Blue channel. 
CH_GREEN 
Green channel. 
CH_RED 
Red channel. 
Definition at line 23 of file CommonFeatureExtractor.h.
Types of the coordinate feature.
Definition at line 12 of file Coordinate.h.

inline 
Linear 1D mapping.
This function perform linear mapping of the value val from one interval to another: \(val\in[min; max]\rightarrow res\in[0; 255]\), such that:
\begin{eqnarray*} min&\rightarrow&0 \\ max&\rightarrow&255 \end{eqnarray*}
val  The value to map. 
min  The lower boundary of the val. 
max  The higher bounday of the val. 
Definition at line 19 of file LinearMapper.h.

inline 
Initializes the square neighborhood structure.
leftGap  Distance from the base point to the neighborhood's left boundary. 
rightGap  Distance from the base point to the neighborhood's right boundary. 
upperGap  Distance from the base point to the neighborhood's upper boundary. 
lowerGap  Distance from the base point to the neighborhood's lower boundary. 
Definition at line 42 of file SquareNeighborhood.h.

inline 
Initializes the square neighborhood structure with a predefine shape.
R  Distance from the base point to the neighborhood's boundaries 
location  Flag describing the location of the base point (Ref. BasePointLocation and Figure 2) 
Definition at line 64 of file SquareNeighborhood.h.

inline 
Initializes the square neighborhood structure with all the same values (base point in the center)
R  Distance from the base point to the neighborhood's boundaries (radius) 
Definition at line 56 of file SquareNeighborhood.h.

inline 
Twolinear 1D mapping.
This function perform linear mapping of the value val from one interval to another: \(val\in[min; max]\rightarrow res\in[0; 255]\), such that:
\begin{eqnarray*} min&\rightarrow&0 \\ mid&\rightarrow&midPoint \\ max&\rightarrow&255 \end{eqnarray*}
For more detail please refer to the Figure 1.
val  The value to map. 
min  The lower boundary of the val. 
max  The higher bounday of the val. 
mid  The xcoordinate of the intersection point, \(mid\in(min; max)\) (Ref. Figure 1). 
midPoint  The ycoordinate of the intersection point, \(midPoint\in[0; 255]\) (Ref. Figure 1). 
Definition at line 38 of file LinearMapper.h.
const float DirectGraphicalModels::fex::SC_LRATE_D = 1e2f 
Learning rate (speed) for dictionary \(D\).
Definition at line 10 of file SparseDictionary.h.
const float DirectGraphicalModels::fex::SC_LRATE_W = 5e2f 
Learning rate (speed) for weights \(W\).
Definition at line 9 of file SparseDictionary.h.