Concept and methodology for automated data preprocessing of object recognition algorithm training
Preparing required data for training object recognition algorithms represents a complex and time-consuming process, that must be avoided especially in industrial environments. The work presented in this paper aims to overcome this challenge through on-line machine learning algorithms, as foundation...
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| Vydáno v: | Procedia CIRP Ročník 104; s. 1791 - 1794 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
2021
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| ISSN: | 2212-8271, 2212-8271 |
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| Abstract | Preparing required data for training object recognition algorithms represents a complex and time-consuming process, that must be avoided especially in industrial environments. The work presented in this paper aims to overcome this challenge through on-line machine learning algorithms, as foundation for further developments and validation. The concept and the developed and validated methodology rely on point clouds resulted from the image processing using a depth camera. The geometry and coordinates of the objects are derived from the point clouds, fact that enables the automation of data preprocessing steps (e.g. manually take the pictures, labelling images), optimizing logistics and production activities. |
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| AbstractList | Preparing required data for training object recognition algorithms represents a complex and time-consuming process, that must be avoided especially in industrial environments. The work presented in this paper aims to overcome this challenge through on-line machine learning algorithms, as foundation for further developments and validation. The concept and the developed and validated methodology rely on point clouds resulted from the image processing using a depth camera. The geometry and coordinates of the objects are derived from the point clouds, fact that enables the automation of data preprocessing steps (e.g. manually take the pictures, labelling images), optimizing logistics and production activities. |
| Author | Giosan, Stefan Matei, Raul Albota, Vlad-Calin Constantinescu, Carmen |
| Author_xml | – sequence: 1 givenname: Stefan surname: Giosan fullname: Giosan, Stefan organization: Fraunhofer IAO, Nobelstraße 12, Stuttgart 70569, Germany – sequence: 2 givenname: Raul surname: Matei fullname: Matei, Raul organization: Fraunhofer IAO, Nobelstraße 12, Stuttgart 70569, Germany – sequence: 3 givenname: Vlad-Calin surname: Albota fullname: Albota, Vlad-Calin email: vlad.albota@iao.fraunhofer.de organization: Fraunhofer IAO, Nobelstraße 12, Stuttgart 70569, Germany – sequence: 4 givenname: Carmen surname: Constantinescu fullname: Constantinescu, Carmen organization: Fraunhofer IAO, Nobelstraße 12, Stuttgart 70569, Germany |
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| Cites_doi | 10.1371/journal.pone.0238802 10.1109/ICRA.2017.7989165 10.1109/SIBGRAPI.2016.017 10.1109/MRA.2010.936956 |
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| Keywords | Image processing Object recognition Machine Learning Logistics |
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| Title | Concept and methodology for automated data preprocessing of object recognition algorithm training |
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