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Active Autonomous Object Modeling for Recognition and Manipulation

Towards a Unified Object Model and Learning Cycle
  • Jens Kubacki
  • Björn Giesler
  • Christopher Parlitz
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

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References

  1. 1.
    Chatila R: The Cognitive Robot Companion and the European Beyond Robotics Initiative. 6th EAJ International Symposium “Living with Robots”, 2004Google Scholar
  2. 2.
    Forsyth DA and Ponce J: Computer Vision: A Modern Approach. Prentice Hall 2002.Google Scholar
  3. 3.
    Miller AT, Knoop S, Allen PK, Christensen HI: Automatic grasp planning using shape primitives. In Proc. IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1824–1829, 2003.Google Scholar
  4. 4.
    Lowe DG: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, Vol. 60, pp. 91–110, 2004.CrossRefGoogle Scholar
  5. 5.
    Roobaert D, Zillich M, Eklundh JO: A Pure Learning Approach to Background-Invariant Object Recognition using Pedagogical Support Vector Learning. CVPR, Vol. 2, No. 2, pp. 351, 2001.Google Scholar
  6. 6.
    Fitzpatrick P, Metta G, Natale L, Rao S and Sandini G: Learning About Objects Through Action — Initial Steps Towards Artificial Cognition. Web page: citeseer.ist.psu.edu/fitzpatrick03learning.htmlGoogle Scholar
  7. 7.
    Oggier T, et al.: An all-solid-state optical range camera for 3D real-time imaging with sub-centimeter depth resolution (SwissRangerTM). In: Proceedings of the SPIE, Vol. 5249, No. 65, 2003.Google Scholar
  8. 8.
    Lukacs G, Marshall AD, and Martin RR: Geometric least-squares fitting of spheres, cylinders, cones and tori. Report GML 1997/5, 1997.Google Scholar
  9. 9.
    Nevada R and Binford TO: Description and recognition of curved objects. Artificial Intelligence, 8(1):77–98, 1977.CrossRefGoogle Scholar
  10. 10.
    Leonardis A, Jaklic A, and Solina F: Superquadrics for segmenting and modeling range data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11): 1289–1295, 1997.CrossRefGoogle Scholar
  11. 11.
    Jaklic A, Leonardis A, and Solina F: Segmentation and Recovery of Superquadrics. Kluwer, 2000.Google Scholar
  12. 12.
    F. Solina and R.K. Bajcsy: Recovery of parametric models from range images: The case for superquadrics with global deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(2):131–147, 1990.CrossRefGoogle Scholar
  13. 13.
    Vapnik, VN: The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1998.Google Scholar
  14. 14.
    Nene SA, Nayar SK and Murase H: Columbia Object Image Library (COIL-100). Technical Report CUCS-006-96, Columbia University, 1996.Google Scholar
  15. 15.
    Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, and Williamson RC: Estimating the support of a high-dimensional distribution. Neural Computation, 13(7): 1443–1471, 2001.CrossRefGoogle Scholar
  16. 16.
    Chang CC and Lin CJ: LIBSVM: a Library for Support Vector Machines (Version 2.31) Wep page: citeseer.ist.psu.edu/chang011ibsvm.htmlGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jens Kubacki
    • 1
  • Björn Giesler
    • 1
  • Christopher Parlitz
    • 1
  1. 1.Fraunhofer IPAUniversity of Karlsruhe (TH)Karlsruhe

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