Skip to main content
SpringerLink
Log in

Komponentenbasierte Bildanalyse zur Identifikation von Objektkategorien

  • Conference paper
Autonome Mobile Systeme 2005

Part of the book series: Informatik aktuell ((INFORMAT))

  • 2887 Accesses

Zusammenfassung

Autonome mobile Greifarme benötigen zur sichtgeführten Manipulation komplexer Gegenstände Vorwissen. Dies kann beispielsweise die exakte geometrische Form der zu manipulierenden Objekte oder die genaue Lage markanter Punkte auf deren Oberfläche sein. Die Handhabung von unbekannten Objekten erfordert einen vorgeschalteten Analyseschritt, bei dem die Form des Objektes erkannt und ein optimaler Griff ermittelt wird. Wissenschaftliche Untersuchungen in den 80er Jahren propagierten, dass das menschliche Gehirn komplexe Objekte erkennt, indem es sie in elementare Teilkomponenten zerlegt. Der vorliegende Beitrag nutzt diese Erkenntnisse zur automatischen Bildanalyse, mit deren Hilfe ein mobiler Manipulator in die Lage versetzt wird, unbekannte Objekte nach einem Rekonstruktionsschritt zu klassifizieren und zu greifen.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literaturverzeichnis

  1. Matsikis A.: Bildgestütztes Teach-In eines mobilen Manipulators in einer virtuellen Umgebung. Disseration, RWTH Aachen, 2005.

    Google Scholar 

  2. Volosyak I, Kouzmitcheva O, Ristic D, Gräser A: Improvement of Visual Perceptual Capabilities by Feedback Structures for Robotic System FRIEND. IEEE Transactions on Systems, Man and Cybernetics, 2005.

    Google Scholar 

  3. Kragic D, Christensen H: Robust Visual Servoing. The International Journal of Robotics Research, Vol. 22, 2003.

    Google Scholar 

  4. Leibe B, Schiele B: Analyzing Appearance and Contour Based Methods for Object Categorization. International Conference on Computer Vision and Pattern Recognition, 2003.

    Google Scholar 

  5. Borges D: 3D Recognition by Parts: A Complete Solution using Parameterized Volumetric Models. IX Simpósio Brasileiro de Comp. Gráfica e Proc. de Imagens (SIBGRAPI), 1996.

    Google Scholar 

  6. Beyer U, Smieja F: A model-based approach to recognition and measurement of partially hidden objects in complex scenes. GMD technical Report, 1996.

    Google Scholar 

  7. Stark L, Bowyer K: Achieving Generalized Object Recognition through Reasoning about Association of Function to Structure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991.

    Google Scholar 

  8. Growe S, Liedtke C, Pakzad K: A Knowledge Based Approach to Sensor Fusion Applied to Multisensory and Multitemporal Imagery. Fourth Int. Airborne Remote Sensing Conference, 1999.

    Google Scholar 

  9. Kasprzak W: Adaptive Computation Methods in Digital Image Sequence Analysis. Elek-tronika series, vol. 127/2000, Warsaw University of Technology Press, 2000.

    Google Scholar 

  10. Biederman I: Recognition-by-components: a theory of human image understanding. Psychological Review, 1987.

    Google Scholar 

  11. Dickinson S, Pentland A, Rosenfeld A: 3-D Shape Recovery Using Distributed Aspect Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 174–198, 1992.

    Article  Google Scholar 

  12. Bergevin D, Levine M: Generic Object Recognition: Building and Matching Coarse Descriptions from Line Drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 1, pp. 19–36, 1993.

    Article  Google Scholar 

  13. Pilu M: Part-based Grouping and Recognition: A Model-Guided Approach. PhD Thesis, University of Edinburgh, 1996.

    Google Scholar 

  14. Moya J: Segmentation of color images for interactive 3d object retrieval. Ph.D. dissertation, RWTH Aachen University, 2004.

    Google Scholar 

  15. Martin W, Aggarwal J: Volumetric descriptions of objects from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 2, pp. 150–158, 1983.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl für Technische Informatik, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Ahornstr. 55, 52074, Aachen

    Florian Bley & Karl-Friedrich Kraiss

Authors
  1. Florian Bley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karl-Friedrich Kraiss
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fakultät Informatik, Elektrotechnik und Informationstechnik Institut für Parallele und Verteilte Systeme (IPVS), Universität Stuttgart, Abt. Bildverstehen Universitätsstr. 38, 70569, Stuttgart

    Paul Levi , Michael Schanz , Reinhard Lafrenz  & Viktor Avrutin , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bley, F., Kraiss, KF. (2006). Komponentenbasierte Bildanalyse zur Identifikation von Objektkategorien. In: Levi, P., Schanz, M., Lafrenz, R., Avrutin, V. (eds) Autonome Mobile Systeme 2005. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-30292-1_8

Download citation

Publish with us

Policies and ethics

Search

Navigation