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DNCFind (CVDNCFind.dll) 14.1
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DNCFind (CVDNCFind.dll) 14.1
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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... | |
Definition of teach parameters which are used to create a DNC-classifier from a CVDNCSAMPLES object.
| 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 |
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 |
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 |
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 |
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 |
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 |
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.