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Memorizing Visual Knowledge for Assembly Process Monitoring

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Pattern Recognition (DAGM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2191))

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Abstract

Machine learning is a desirable property of computer vision systems. Especially in process monitoring knowledge of temporal context speeds up recognition. Moreover, memorizing earlier results allows to establish qualitative relations between the stages of a processes. In this contribution we present an architecture that learns different visual aspects of assemblies. It is organized hierarchically and stores prototypical data from different levels of image processing and object recognition. An example underlines that this memory facilitates assembly recognition and recognizes structural relations among complex objects.

This work has been supported by the DFG within SFB 360.

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Author information

Authors and Affiliations

  1. Technische Fakultät, AG Angewandte Informatik, Bielefeld University, P.O. Box 100131, 33501, Bielefeld, Germany

    Christian Bauckhage, Jannik Fritsch & Gerhard Sagerer

Authors
  1. Christian Bauckhage
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  2. Jannik Fritsch
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  3. Gerhard Sagerer
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Editor information

Editors and Affiliations

  1. Chair of Image Understanding and Knowledge Based Systems, Technical University Munich, Orleansstr. 34, 81667, Munich, Germany

    Bernd Radig  & Stefan Florczyk  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Bauckhage, C., Fritsch, J., Sagerer, G. (2001). Memorizing Visual Knowledge for Assembly Process Monitoring. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_24

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  • DOI: https://doi.org/10.1007/3-540-45404-7_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42596-0

  • Online ISBN: 978-3-540-45404-5

  • eBook Packages: Springer Book Archive

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