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Object detection in unstructured 3D data sets using explicit semantics

Abstract : With the evolution of technologies and robotics, the possibilities offered by 3D acquisition systems have increased. Nowadays, these systems are used in different domains as for autonomous vehicles, rescue robots, cultural heritage, for example. These application fields often require to perform object recognition from acquired data. Therefore, various methodologies have been investigated to automatically process 3D point cloud data in order to detect contained objects. The best methodologiesdepend on the context, that means they are specific to the data to be processed and the objects to be recognized. They produce efficient recognition, which is essential whatever the application field. However, adapting methodologies to a particular application field or use case limits the flexibility to extend the use of a method to other fields. These observations highlight the importance of developing object recognition methodologies specific to a detection context, but also the limitation of existing methods to preserve their capacity within changing detection contexts. An excellent example of a high degree of flexibility to changing contexts is human intelligence and human’s ability to design ad hoc methodologies. Humans can analyze the context according to their knowledge and combine different characteristics or strategies according to the objective to be achieved. It would, therefore, be helpful for Computer Vision tools to integrate elements of artificial intelligence, allowing to adapt to the context of an application fields and to guide the detection process in this respect. This Ph.D. thesis presents a knowledge-based approach for object recognition that can be used whatever the application field. Its architecture is based on semantic technologies to allow a knowledge management module to guide the objects detection process through a step by step procedure performing the selection, parameterization, and execution of algorithms. The detection process is performed thanks to an artificial intelligence approach that uses explicit knowledge to design a context-dependent object recognition solution. Its strength is its adaptability to the context, but also its capability to analyze and understand a scene and contained objects and the specificities of the data to be processed. This understanding capability is realized through a self-learning process able to define and validate hypotheses concerning the context, also enabling to enrich the knowledge base and to improve the objects recognition process. The efficiency of this adaptation capability will be demonstrated in four use cases from different application fields. The first use case is an indoor of a building. It is used for a monitoring purpose. The second use case is located in the field of Archaeology represented by ancient ruins containing a terrace house with a watermill. The third use case is an outdoor representing a part of the city of Freiburg in Germany. It is used for an industrial purpose. Finally, the last use case is an indoor acquired by Microsoft’s Kinect. It is used for a robotic purpose.
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Jean-Jacques Ponciano. Object detection in unstructured 3D data sets using explicit semantics. Artificial Intelligence [cs.AI]. Université de Lyon, 2019. English. ⟨NNT : 2019LYSES059⟩. ⟨tel-02497452⟩

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