Manual classification of Gaussian Mixture Models
Plugin : SimpleForestNom de classe : SF_StepManualClassifyClusters
Description
Output clusters of the step Gaussian Mixture Models with Fast Point Feature Histograms should be inspected before executing this step. Each cluster needs to be classified as good, noise or unclassified. All good clusters are merged as well as all noise clusters. Points in unclassified clusters are allocated afterwards regarding their distance to their nearest good and noise neighbors.
Paramètres
Paramètres de pré-configuration (non modifiables une fois l'étape ajoutée) :
Please excuse potential double citation with the step related citation for the
following general citation for SimpleForest (work on updated citable resource ongoing):
A majority of implementations are based on PCL library:
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- Uncheck to deactivate parameterization possibilities of this step. Only recommended for beginners: Activé.
- Compute for each unclassfied point the average distance of [k] nearest neighbors within noise and good points: 9.
- [Classification] of the cluster number 0: Unclassified .
- [Classification] of the cluster number 1: Unclassified .
- [Classification] of the cluster number 2: Unclassified .
- [Classification] of the cluster number 3: Unclassified .
- [Classification] of the cluster number 4: Unclassified .
- [Classification] of the cluster number 5: Unclassified .
- [Classification] of the cluster number 6: Unclassified .
- [Classification] of the cluster number 7: Unclassified .
- [Classification] of the cluster number 8: Unclassified .
- [Classification] of the cluster number 9: Unclassified .
Please excuse potential double citation with the step related citation for the
following general citation for SimpleForest (work on updated citable resource ongoing):
A majority of implementations are based on PCL library:
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Données d'entrée
Structure des données d'entrée recherchées :
Result : Point Cloud
...
Cloud Group (Group)
Cluster Group number0 (Group)
Cluster number 0 (Item with points)
Cluster Group number1 (Group)
Cluster number 1 (Item with points)
Cluster Group number2 (Group)
Cluster number 2 (Item with points)
Cluster Group number3 (Group)
Cluster number 3 (Item with points)
Cluster Group number4 (Group)
Cluster number 4 (Item with points)
Cluster Group number5 (Group)
Cluster number 5 (Item with points)
Cluster Group number6 (Group)
Cluster number 6 (Item with points)
Cluster Group number7 (Group)
Cluster number 7 (Item with points)
Cluster Group number8 (Group)
Cluster number 8 (Item with points)
Cluster Group number9 (Group)
Cluster number 9 (Item with points)
Cloud (Item with points)
Result : Point Cloud
...
Cloud Group (Group)
Cluster Group number0 (Group)
Cluster number 0 (Item with points)
Cluster Group number1 (Group)
Cluster number 1 (Item with points)
Cluster Group number2 (Group)
Cluster number 2 (Item with points)
Cluster Group number3 (Group)
Cluster number 3 (Item with points)
Cluster Group number4 (Group)
Cluster number 4 (Item with points)
Cluster Group number5 (Group)
Cluster number 5 (Item with points)
Cluster Group number6 (Group)
Cluster number 6 (Item with points)
Cluster Group number7 (Group)
Cluster number 7 (Item with points)
Cluster Group number8 (Group)
Cluster number 8 (Item with points)
Cluster Group number9 (Group)
Cluster number 9 (Item with points)
Cloud (Item with points)
Références
Hackenberg Jan, Spiecker Heinrich, Calders Kim, Disney Mathias, Raumonen Pasi. 2015. SimpleTree - an efficient open source tool to build tree models from TLS clouds. Multidisciplinary Digital Publishing Institute. Forests.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.