Parameters for teaching a DNC finder.
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#include <cvb/dnc/teach_parameters.hpp>
Parameters for teaching a DNC finder.
◆ TeachParameters()
Default teach parameters.
- Exceptions
-
| Does | not throw any exception. |
◆ DerivativePatchSize()
| int DerivativePatchSize |
( |
| ) |
const |
|
inlinenoexcept |
Get smoothing area in pixels for gradient and normal calculation.
- Returns
- The patch size.
- Exceptions
-
| Does | not throw any exception. |
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 of Cvb.Dnc.SearchParameters.DerivativePatchSize of a detection task should be equal, or - in case of sensor noise - bigger. The minimum value is 3.
◆ DistanceKernelSize()
| double DistanceKernelSize |
( |
| ) |
const |
|
inlinenoexcept |
Get the factor for Distance transform calculation.
- Returns
- The distance kernel size.
- Exceptions
-
| Does | not throw any exception. |
A so called Huber kernel is used for the calculation of a distance transform of an example. To some extend a Huber kernel can possibly enlarge the convergence basin of ICP-operations, relative to the Euclidian distance transform (factor = 1). A recommended value is 2. The minimum value is 1.
◆ HeightSensitivity()
| double HeightSensitivity |
( |
| ) |
const |
|
inlinenoexcept |
Get the minimum gradient magnitude to accept a local gradient as a feature in the depth images.
- Returns
- The height sensitivity.
- Exceptions
-
| Does | not throw any exception. |
The value specifies a threshold (in millimeters) above which local height changes are observed as characteristic features. A typical value is 1 mm. This value is also used implicitly during later search tasks. The minimum value is 0.0.
◆ ICPShrink()
Get the subsample factor for ICP.
- Returns
- The subsample factor.
- Exceptions
-
| Does | not throw any exception. |
To speed up search tasks, a voxel representation of the examples is pre-calculated. The subsample factor determins 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.
◆ LocalDistributionSize()
| int LocalDistributionSize |
( |
| ) |
const |
|
inlinenoexcept |
Get the size of area in which local features are distributed (in pixels).
- Returns
- The local distribution size.
- Exceptions
-
| Does | not throw any exception. |
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()
| int NumGradientFeatures |
( |
| ) |
const |
|
inlinenoexcept |
Get the number of gradient features retained in the classifier.
- Returns
- The number of gradient features.
- Exceptions
-
| Does | not throw any exception. |
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. The minimum value is 0.
◆ NumNormalFeatures()
| int NumNormalFeatures |
( |
| ) |
const |
|
inlinenoexcept |
Get the number of normal vector features retained in the classifier.
- Returns
- The number of normal vector features.
- Exceptions
-
| Does | not throw any exception. |
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. The minimum value is 0.
◆ SetDerivativePatchSize()
| void SetDerivativePatchSize |
( |
int |
value | ) |
|
|
inline |
Set smoothing area in pixels for gradient and normal calculation.
- Parameters
-
- Exceptions
-
| Does | not throw any exception. |
◆ SetDistanceKernelSize()
| void SetDistanceKernelSize |
( |
double |
value | ) |
|
|
inline |
Set the factor for Distance transform calculation.
- Parameters
-
| [in] | value | The distance kernel size. |
- Exceptions
-
| Does | not throw any exception. |
◆ SetHeightSensitivity()
| void SetHeightSensitivity |
( |
double |
value | ) |
|
|
inline |
Set the minimum gradient magnitude to accept a local gradient as a feature in the depth images.
- Parameters
-
| [in] | value | The height sensitivity. |
- Exceptions
-
| Does | not throw any exception. |
◆ SetICPShrink()
| void SetICPShrink |
( |
int |
value | ) |
|
|
inline |
Set the subsample factor for ICP.
- Parameters
-
| [in] | value | The subsample factor. |
- Exceptions
-
| Does | not throw any exception. |
◆ SetLocalDistributionSize()
| void SetLocalDistributionSize |
( |
int |
value | ) |
|
|
inline |
Set the size of area in which local features are distributed (in pixels).
- Parameters
-
| [in] | value | The local distribution size. |
- Exceptions
-
| Does | not throw any exception. |
◆ SetNumGradientFeatures()
| void SetNumGradientFeatures |
( |
int |
value | ) |
|
|
inline |
Set the number of gradient features retained in the classifier.
- Parameters
-
| [in] | value | The number of gradient features. |
- Exceptions
-
| Does | not throw any exception. |
◆ SetNumNormalFeatures()
| void SetNumNormalFeatures |
( |
int |
value | ) |
|
|
inline |
Set the number of normal vector features retained in the classifier.
- Parameters
-
| [in] | value | The number of normal vector features. |
- Exceptions
-
| Does | not throw any exception. |
