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Open AccessArticle

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

1
i3mainz, Institute for Spatial Information and Surveying Technology Mainz University of Applied Sciences, D-55128 Mainz, Germany
2
Hubert Curien Laboratory, University Jean Monnet, 18 Rue Professeur Benoît Lauras, 42100 Saint-Etienne, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 442; https://doi.org/10.3390/ijgi8100442
Received: 17 August 2019 / Revised: 17 September 2019 / Accepted: 29 September 2019 / Published: 8 October 2019
In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.
Keywords: object detection; semantic; OWL; SPARQL; ontology; point cloud processing object detection; semantic; OWL; SPARQL; ontology; point cloud processing
MDPI and ACS Style

Ponciano, J.-J.; Trémeau, A.; Boochs, F. Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes. ISPRS Int. J. Geo-Inf. 2019, 8, 442.

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