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DAGM German Conference on Pattern Recognition

DAGM GCPR 2021: Pattern Recognition pp 453–466Cite as

Quantifying Uncertainty of Image Labelings Using Assignment Flows

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13024))

Abstract

This paper introduces a novel approach to uncertainty quantification of image labelings determined by assignment flows. Local uncertainties caused by ambiguous data and noise are estimated by fitting Dirichlet distributions and pushed forward to the tangent space. The resulting first- and second-order moments are then propagated using a linear ODE parametrization of assignment flows. The corresponding moment evolution equations can be solved in closed form and numerically evaluated using iterative Krylov subspace techniques and low-rank approximation. This results in a faithful representation and quantification of uncertainty in the output space of image labelings, which is important in all applications where confidence in pixelwise decisions matters.

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Acknowledgement

This work is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster).

Author information

Authors and Affiliations

  1. Image and Pattern Analysis Group, Heidelberg University, Heidelberg, Germany

    Daniel Gonzalez-Alvarado, Alexander Zeilmann & Christoph Schnörr

  2. Heidelberg Collaboratory for Image Processing, Heidelberg University, Heidelberg, Germany

    Daniel Gonzalez-Alvarado & Christoph Schnörr

Authors
  1. Daniel Gonzalez-Alvarado
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  2. Alexander Zeilmann
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  3. Christoph Schnörr
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Correspondence to Daniel Gonzalez-Alvarado .

Editor information

Editors and Affiliations

  1. Fraunhofer IAIS, Sankt Augustin, Germany

    Christian Bauckhage

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Alexander Schwing

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Gonzalez-Alvarado, D., Zeilmann, A., Schnörr, C. (2021). Quantifying Uncertainty of Image Labelings Using Assignment Flows. In: Bauckhage, C., Gall, J., Schwing, A. (eds) Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science(), vol 13024. Springer, Cham. https://doi.org/10.1007/978-3-030-92659-5_29

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  • DOI: https://doi.org/10.1007/978-3-030-92659-5_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92658-8

  • Online ISBN: 978-3-030-92659-5

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