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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))

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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.

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Author information

Authors and Affiliations

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

    Frederic Jurie

Authors
  1. Frederic Jurie
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Editor information

Roland Chin Ting-Chuen Pong

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© 1997 Springer-Verlag Berlin Heidelberg

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63931-2

  • Online ISBN: 978-3-540-69670-4

  • eBook Packages: Springer Book Archive

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