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International Conference on Computer Vision Systems

ICVS 2003: Computer Vision Systems pp 12–21Cite as

Efficient Pose Estimation Using View-Based Object Representations

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2626))

Abstract

We present an efficient method for estimating the pose of a three-dimensional object. Its implementation is embedded in a computer vision system which is motivated by and based on cognitive principles concerning the visual perception of three-dimensional objects. Viewpoint-invariant object recognition has been subject to controversial discussions for a long time. An important point of discussion is the nature of internal object representations. Behavioral studies with primates, which are summarized in this article, support the model of view-based object representations. We designed our computer vision system according to these findings and demonstrate that very precise estimations of the poses of real-world objects are possible even if only a few number of sample views of an object is available. The system can be used for a variety of applications.

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References

  1. S. Edelman and H. H. Bülthoff. Orientation Dependence in the Recognition of Familiar and Novel Views of Three-Dimensional Objects. Vision Research, 32(12):2385–2400, 1992.

    Article  Google Scholar 

  2. M. J. Tarr. Orientation Dependence in Three-Dimensional Object Recognition. Ph.D. Thesis, MIT, 1989.

    Google Scholar 

  3. F. Cutzu and S. Edelman. Canonical Views in Object Representation and Recognition. Vision Research, 34:3037–3056, 1994.

    Article  Google Scholar 

  4. N. K. Logothetis, J. Pauls, H. H. Bülthoff, and Poggio T. View-Dependent Object Recognition by Monkeys. Current Biology, 4:401–414, 1994.

    Article  Google Scholar 

  5. M. Wexler, S. M. Kosslyn, and A. Berthoz. Motor processes in mental rotation. Cognition, 68: 77–94, 1998.

    Article  Google Scholar 

  6. N. K. Logothetis, J. Pauls, and Poggio T. Shape Representation in the Inferior Temporal Cortex of Monkeys. Current Biology, 5(5):552–563, 1995.

    Article  Google Scholar 

  7. D. C. Burr, M. C. Morrone, and D. Spinelli. Evidence for Edge and Bar Detectors in Human Vision. Vision Research, 29(4):419–431, 1989.

    Article  Google Scholar 

  8. J. P. Jones and L. A. Palmer. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58(6):1233–1258, 1987.

    Google Scholar 

  9. C. Eckes and J. C. Vorbrüggen. Combining Data-Driven and Model-Based Cues for Segmentation of Video Sequences. In Proc. WCNN96, pages 868–875, 1996.

    Google Scholar 

  10. T. Maurer and C. von der Malsburg. Tracking and Learning Graphs and Pose on Image Sequences of Faces. In Proc. Int. Conf. on Automatic Face-and Gesture-Recognition, pages 176–181, 1996.

    Google Scholar 

  11. V. Chvatal. A Greedy Heuristic for the Set-Covering Problem. Mathematics of Operations Research, 4(3):233–235, 1979.

    Article  MATH  MathSciNet  Google Scholar 

  12. R. Horaud, B. Conio, O. Leboulleux, and B. Lacolle. An Analytic Solution for the Perspective 4-Point Problem. Computer Vision, Graphics and Image Processing, 47:33–44, 1989.

    Article  Google Scholar 

  13. M. Dhome, M. Richetin, J. Lapreste, and G. Rives. Determination of the Attitude of 3-D Objects from a Single Perspective View. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12):1265–1278, 1989.

    Article  Google Scholar 

  14. R. M. Haralick, C. Lee, K. Ottenberg, and M. Nölle. Analysis and Solutions of the Three Point Perspective Pose Estimation Problem. In Proc. of the IEEE Comp. Society Conf. on Computer Vision and Pattern Recognition, pages 592–598, 1991.

    Google Scholar 

  15. D. G. Lowe. Three-Dimensional Object Recognition from Single Two-Dimensional Images. Artificial Intelligence, 31:355–395, 1987.

    Article  Google Scholar 

  16. J. Yuan. A General Photogrammetric Method for Determining Object Position and Orientation. IEEE Journal of Robotics and Automation, 5(2):129–142, 1989.

    Article  Google Scholar 

  17. M. Pötzsch. Die Behandlung der Wavelet-Transformation von Bildern in der Nähe von Objektkanten. Technical Report IRINI 94-04, Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany, 1994.

    Google Scholar 

  18. S. Ullman and R. Basri. Recognition by Linear Combinations of Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):992–1006, 1991.

    Article  Google Scholar 

  19. G. Peters and C. von der Malsburg. View Reconstruction by Linear Combination of Sample Views. In Proc. BMVC 2001, pages 223–232, 2001.

    Google Scholar 

  20. M. Lades, J. C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Würtz, and W. Konen. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. Comp., 42: 300–311, 1993.

    Article  Google Scholar 

  21. G. Peters. A View-Based Approach to Three-Dimensional Object Perception. Ph.D. Thesis, Shaker Verlag, Aachen, Germany, 2002.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Informatik VII, Universität Dortmund, Otto-Hahn-Str. 16, D-44227, Dortmund, Germany

    Gabriele Peters

Authors
  1. Gabriele Peters
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Editor information

Editors and Affiliations

  1. INRIA Rhône-Alpes, 655 Ave de l’Europe, 38330, Montbonnot, France

    James L. Crowley

  2. Montefiore Institute, University of Liège, 4000, Liège Sart-Tilman, Belgium

    Justus H. Piater

  3. Automation and Control Institute, Vienna University of Technology, Gusshausstraße 27/376, 1040, Vienna, Austria

    Markus Vincze

  4. Institute of Digital Image Processing, Joanneum Research, Wastiangasse 6, 8010, Graz, Austria

    Lucas Paletta

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© 2003 Springer-Verlag Berlin Heidelberg

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Peters, G. (2003). Efficient Pose Estimation Using View-Based Object Representations. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_2

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  • DOI: https://doi.org/10.1007/3-540-36592-3_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00921-4

  • Online ISBN: 978-3-540-36592-1

  • eBook Packages: Springer Book Archive

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