Skip to main content
Springer Nature Link
Log in

Robust hypothesis verification for model based object recognition using Gaussian error model

  • Session S3A: Onject Recognition and Modeling
  • Conference paper
  • First Online:
Computer Vision — ACCV'98 (ACCV 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1352))

Included in the following conference series:

  • 2670 Accesses

Abstract

The use of hypothesis verification is recurrent in the model based recognition literature. Small sets of features forming salient groups are paired with model features. Poses can be hypothesised from this small set of feature-to-feature correspondences. The verification of the pose consists in measuring how much model features transformed by the computed pose coincide with image features. When data involved in the initial pairing are noisy the pose is inaccurate and the verification is a difficult problem.

In this paper we propose a robust hypothesis verification algorithm, assuming data error is Gaussian. We present experimental results obtained with 2D and 3D recognition proving that the proposed algorithm is fast and robust.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Breuel, T.: 1992, Fast recognition using adaptive subdivisions of transformation space, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Champain, Illinois, pp. 445–451.

    Google Scholar 

  • Brunelli, R. and Falavigna, D.: 1995, Person identification using multiple cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 955–966.

    Google Scholar 

  • Califano, A. and Mohan, R.: 1994, Mulidimensional indexing for recognizing visual shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence 16(4),373–392.

    Google Scholar 

  • DeMenthon, D. and Davis, L.: 1992, Model-based object pose in 25 lines of code, Proc. European Conference on Computer Vision, Santa Margherita Ligure, Italy, pp. 19–22.

    Google Scholar 

  • Fletcher, R.: 1987, Practical Methods of Optimization, wiley-interscience publications edn, John Wiley and Sons, New York.

    Google Scholar 

  • Gandhi, T. and Camps, O.: 1994, Robust feature selection for object recognition using uncertain 2d image data, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Seatle, Washington, pp. 281–287.

    Google Scholar 

  • Crimson, W.: 1991, The combinatorics of heuristic search term for object recognition in cluttered environment, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(9), 920–935.

    Google Scholar 

  • Huttenlocher, D. and Ullman, S.: 1990, Recognizing solid objects by alignment with an image, International Journal of Computer Vision 5(2), 195–212.

    Google Scholar 

  • Huttenlocher, D., Klanderman, G. and Rucklidge, W.: 1993, Comparing images using the hausdorff distance, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863.

    Google Scholar 

  • Jacobs, D.: 1996, Robust and efficient detection of salient convex groups, 18(1), 541–548.

    Google Scholar 

  • Murase, H. and Nayar, S.: 1995, Visual learning and recognition od 3d object from appearance, International Journal of Computer Vision 18(14), 5–24.

    Google Scholar 

  • Olson, C.: 1993, Probabilistic indexing: A new method of indexing 3d model data from 2d image data, Technical report, University of California at Berkeley.

    Google Scholar 

  • Pentland, A., Moghadam, B. and Starner, T.: 1994, View-based and modular eigenspaces for face recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Seatle, Washington, pp. 84–91.

    Google Scholar 

  • Sarachik, K. and Crimson, W.: 1993, Gaussian error models for object recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition, New-York, pp. 400–406.

    Google Scholar 

  • Sclaroff, S. and Pentland, A.: 1995, Modal matching for correspondence and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 545–561.

    Google Scholar 

  • ter Haar Romeny, B., Florack, L., Salden, A. and Viergever, M.: 1994, Higher order differential structure of images, Image and Vision Computing 12(6), 317–325.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LASMEA - CNRS UNIR 6602, Université Blaise-Pascal, F-63177, Aubière, France

    Frederic Jurie

Authors
  1. Frederic Jurie
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Roland Chin Ting-Chuen Pong

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jurie, F. (1997). Robust hypothesis verification for model based object recognition using Gaussian error model. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63931-4_247

Download citation

  • DOI: https://doi.org/10.1007/3-540-63931-4_247

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63931-2

  • Online ISBN: 978-3-540-69670-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics