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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 366–381Cite as

Global Motion Estimation from Relative Measurements in the Presence of Outliers

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

Abstract

This work addresses the generic problem of global motion estimation (homographies, camera poses, orientations, etc.) from relative measurements in the presence of outliers. We propose an efficient and robust framework to tackle this problem when motion parameters belong to a Lie group manifold. It exploits the graph structure of the problem as well as the geometry of the manifold. It is based on the recently proposed iterated extended Kalman filter on matrix Lie groups. Our algorithm iteratively samples a minimum spanning tree of the graph, applies Kalman filtering along this spanning tree and updates the graph structure, until convergence. The graph structure update is based on computing loop errors in the graph and applying a proposed statistical inlier test on Lie groups. This is done efficiently, taking advantage of the covariance matrix of the estimation errors produced by the filter. The proposed formalism is applied on both synthetic and real data, for a camera pose registration problem, an automatic image mosaicking problem and a partial 3D reconstruction merging problem. In these applications, the framework presented in this paper efficiently recovers the global motions while the state of the art algorithms fail due to the presence of a large proportion of outliers.

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Notes

  1. 1.

    We use the Matlab library Matgraph [35] to find the path between \(\mathfrak {m}\) and \(\mathfrak {n}\).

  2. 2.

    The supplementary material and the Matlab code are available at https://sites.google.com/site/guillaumebourmaud/.

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Acknowledgment

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 288199 - Dem@Care. The authors would like to thank the reviewers, Moncef Hidane and Cornelia Vacar for their valuable help.

Author information

Authors and Affiliations

  1. University Bordeaux, IMS, UMR 5218, 33400, Talence, France

    Guillaume Bourmaud, Rémi Mégret, Audrey Giremus & Yannick Berthoumieu

  2. CNRS, IMS, UMR 5218, 33400, Talence, France

    Guillaume Bourmaud, Rémi Mégret, Audrey Giremus & Yannick Berthoumieu

  3. Department of Mathematical Sciences, University of Puerto-Rico at Mayaguez, Mayaguez, USA

    Rémi Mégret

Authors
  1. Guillaume Bourmaud
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  2. Rémi Mégret
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  3. Audrey Giremus
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  4. Yannick Berthoumieu
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Corresponding author

Correspondence to Guillaume Bourmaud .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Bourmaud, G., Mégret, R., Giremus, A., Berthoumieu, Y. (2015). Global Motion Estimation from Relative Measurements in the Presence of Outliers. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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