DNCFind (CVDNCFind.dll) 15.0
CVDNCTeachParams Struct Reference

Definition of teach parameters which are used to create a DNC-classifier from a CVDNCSAMPLES object. More...

Data Fields

int DerivativePatchSize
 Smoothing area in pixels for gradient and normal calculation. More...
 
double DistanceKernelSize
 Factor for Distance transform calculation. More...
 
double HeightSensitivity
 Minimum gradient magnitude to accept a local gradient as a feature in the depth images. More...
 
int ICPShrink
 ICP-subsample factor. More...
 
int LocalDistributionSize
 Size of area in which local features are distributed (in pixels). More...
 
int NumGradientFeatures
 Number of gradient features retained in the classifier. More...
 
int NumNormalFeatures
 Number of normal vector features retained in the classifier. More...
 

Detailed Description

Definition of teach parameters which are used to create a DNC-classifier from a CVDNCSAMPLES object.

See also
CVDNCFINDER, CVDNCCreateFromSamples, CVDNCGetTeachParams

Field Documentation

◆ DerivativePatchSize

DerivativePatchSize

Smoothing area in pixels for gradient and normal calculation.

The value controls the local environment in the depth images of the examples which is used to calculate gradients and normals. Minimum is 3. Larger values effectively result in a smoothing of the depth images. The value should be odd. Typical values are in the range 3..9. Smaller values should be favored, since the example images of the CAD object are flawless and free of noise.

The corresponding value CVDNCSearchParams::DerivativePatchSize in the SearchParameters of a detection task (see CVDNCFind) should be equal, or - in case of sensor noise - bigger.

◆ DistanceKernelSize

DistanceKernelSize

Factor for Distance transform calculation.

A so called Huber kernel is used for the calculation of a distance transform of an example. To some extend a Huber kernel bigger than 1.0 can possibly enlarge the convergence basin of ICP-operations, relative to the Euclidian distance transform (Huber kernel = 1).

A recommended value is 2.0, the minimum allowed value is 1.0.

◆ HeightSensitivity

HeightSensitivity

Minimum gradient magnitude to accept a local gradient as a feature in the depth images.

The value specifies a threshold (in millimeters) above which local height changes are observed as characteristic features. A typical value is 1 mm, the minimum value is 0.

This value is also used implicitly during later search tasks.

◆ ICPShrink

ICPShrink

ICP-subsample factor.

To speed up search tasks, a voxel representation of the examples is pre-calculated. The subsample factor determines the amount of data used to perform an ICP-operation. It strongly influences the recognition speed and the amount of memory used. The recommended value is 4. The minimum value is 1, resulting in no subsampling.

◆ LocalDistributionSize

LocalDistributionSize

Size of area in which local features are distributed (in pixels).

To account for the finite and discrete example positions the local features are somewhat smeared out. This value specifies the local active range of a single feature. A typical value is 8, the minimum value is 0.

◆ NumGradientFeatures

NumGradientFeatures

Number of gradient features retained in the classifier.

To speed up the calculation of correspondences during a detection task, the possibly dense gradient features from the examples are thinned out to a minimum necessary number.

This number strongly depends on the object itself. Typical values are between 100 and 1000. A value of 0 means that all gradient features should be used.

◆ NumNormalFeatures

NumNormalFeatures

Number of normal vector features retained in the classifier.

To speed up the calculation of correspondences during a detection task, the dense normal features from the examples are thinned out to a minimum necessary number.

This number strongly depends on the object itself. Typical values are between 100 and 1000. A value of 0 means that all normal features should be used.