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Discriminative Detection and Alignment in Volumetric Data

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 8142))

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Abstract

In this paper, we aim for detection and segmentation ofArabidopsis thaliana cells in volumetric image data. To this end, we cluster the training samples by their size and aspect ratio and learn a detector and a shape model for each cluster. While the detector yields good cell hypotheses, additionally aligning the shape model to the image allows to better localize the detections and to reconstruct the cells in case of low quality input data. We show that due to the more accurate localization, the alignment also improves the detection performance.

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Author information

Authors and Affiliations

  1. Lehrstuhl für Mustererkennung und Bildverabeitung, Institut für Informatik, Germany

    Dominic Mai, Philipp Fischer, Thomas Brox & Olaf Ronneberger

  2. BIOSS Centre of Biological Signalling Studies, Germany

    Dominic Mai, Klaus Palme, Thomas Brox & Olaf Ronneberger

  3. Institut für Biologie II, Albert-Ludwigs-Universität Freiburg, Germany

    Jasmin Dürr & Klaus Palme

  4. INRA Versailles, France

    Thomas Blein

Authors
  1. Dominic Mai

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  2. Philipp Fischer

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  3. Thomas Blein

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  4. Jasmin Dürr

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  5. Klaus Palme

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  6. Thomas Brox

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  7. Olaf Ronneberger

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Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Computer Science, Saarland University, Campus E1.7, 66041, Saarbrücken, Germany

    Joachim Weickert

  2. Faculty of Mathematics and Computer Science, Saarland University, Campus E1.3, 66041, Saarbrücken, Germany

    Matthias Hein

  3. Computer Vision and Multimodal Computing, Max-Planck-Institute for Informatics, Campus E 1.4, 66123, Saarbrücken, Germany

    Bernt Schiele

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© 2013 Springer-Verlag Berlin Heidelberg

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Cite this paper

Mai, D.et al. (2013). Discriminative Detection and Alignment in Volumetric Data. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_21

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Chapter
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eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
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Softcover Book
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  • Compact, lightweight edition
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