KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS

Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools availab...

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Main Authors: F. Boochs, A. Karmacharya, A. Marbs
Format: Article
Language:English
Published: Copernicus Publications 2012-07-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/91/2012/isprsarchives-XXXIX-B3-91-2012.pdf
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author F. Boochs
A. Karmacharya
A. Marbs
author_facet F. Boochs
A. Karmacharya
A. Marbs
author_sort F. Boochs
collection DOAJ
description Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This “understanding” enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists’ knowledge of the scene and algorithmic processing.
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spelling doaj.art-1a3a82c3660040fcbae29a74a8304a092022-12-21T20:19:06ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342012-07-01XXXIX-B3919610.5194/isprsarchives-XXXIX-B3-91-2012KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDSF. Boochs0A. Karmacharya1A. Marbs2i3mainz, Insitute for Spatial Information and Surveying Technology, University of Applied Sciences Mainz, Lucy-Hillebrand-Str. 2, 55128 Mainz, Germanyi3mainz, Insitute for Spatial Information and Surveying Technology, University of Applied Sciences Mainz, Lucy-Hillebrand-Str. 2, 55128 Mainz, Germanyi3mainz, Insitute for Spatial Information and Surveying Technology, University of Applied Sciences Mainz, Lucy-Hillebrand-Str. 2, 55128 Mainz, GermanyObject identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This “understanding” enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists’ knowledge of the scene and algorithmic processing.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/91/2012/isprsarchives-XXXIX-B3-91-2012.pdf
spellingShingle F. Boochs
A. Karmacharya
A. Marbs
KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
title_full KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
title_fullStr KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
title_full_unstemmed KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
title_short KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS
title_sort knowledge based object detection in laser scanning point clouds
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/91/2012/isprsarchives-XXXIX-B3-91-2012.pdf
work_keys_str_mv AT fboochs knowledgebasedobjectdetectioninlaserscanningpointclouds
AT akarmacharya knowledgebasedobjectdetectioninlaserscanningpointclouds
AT amarbs knowledgebasedobjectdetectioninlaserscanningpointclouds