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Robust Interactive Multi-label Segmentation with an Advanced Edge Detector

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

Recent advances on convex relaxation methods allow for a flexible formulation of many interactive multi-label segmentation methods. The building blocks are a likelihood specified for each pixel and each label, and a penalty for the boundary length of each segment. While many sophisticated likelihood estimations based on various statistical measures have been investigated, the boundary length is usually measured in a metric induced by simple image gradients. We show that complementing these methods with recent advances of edge detectors yields an immense quality improvement. A remarkable feature of the proposed method is the ability to correct some erroneous labels, when computer generated initial labels are considered. This allows us to improve state-of-the-art methods for motion segmentation in videos by 5–10 % with respect to the F-measure (Dice score).

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Notes

  1. 1.

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Acknowledgements

This work was partially funded by the European Union’s FP7 under the project Computational Horizons in Cancer (grant agreement No. 600841).

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Authors and Affiliations

  1. Department of Pediatric Oncology and Hematology, Saarland University Hospital, 66421, Homburg, Germany

    Sabine Müller & Norbert Graf

  2. Mathematical Image Analysis Group, Saarland University, Campus E1.7, 66041, Saarbrücken, Germany

    Sabine Müller, Peter Ochs & Joachim Weickert

Authors
  1. Sabine Müller
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  2. Peter Ochs
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  3. Joachim Weickert
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  4. Norbert Graf
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Corresponding author

Correspondence to Sabine Müller .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Müller, S., Ochs, P., Weickert, J., Graf, N. (2016). Robust Interactive Multi-label Segmentation with an Advanced Edge Detector. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

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