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CVBpy 15.0
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CVBpy 15.0
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Parameters for teaching a Match3D DNC finder. More...
Inherits object.
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| derivative_patch_size = property | |
| int: Get or set smoothing area in pixels for gradient and normal calculation. More... | |
| distance_kernel_size = property | |
| float: Get or set the factor for Distance transform calculation. More... | |
| height_sensitivity = property | |
| float: Get or set the minimum gradient magnitude to accept a local gradient as a feature in the depth images. More... | |
| icp_shrink = property | |
| int: Get or set the subsample factor for ICP. More... | |
| local_distribution_size = property | |
| int: Get or set the size of area in which local features are distributed (in pixels). More... | |
| num_gradient_features = property | |
| int: Get or set the number of gradient features retained in the classifier. More... | |
| num_normal_features = property | |
| int: Get or set the number of normal vector features retained in the classifier. More... | |
Parameters for teaching a Match3D DNC finder.
Default teach parameters.
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int: Get or set smoothing area in pixels for gradient and normal calculation.
This 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. This 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 SearchParameters.derivative_patch_size of a detection task should be equal, or - in case of sensor noise - bigger.
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float: Get or set the 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 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.
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float: Get or set the minimum gradient magnitude to accept a local gradient as a feature in the depth images.
This 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. This value is also used implicitly during later search tasks.
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int: Get or set the subsample factor for ICP.
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, resulting in no subsampling.
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int: Get or set the 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.
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int: Get or set the 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.
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int: Get or set the 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.