- Dominic Mai19,20,
- Philipp Fischer19,
- Thomas Blein22,
- Jasmin Dürr21,
- Klaus Palme20,21,
- Thomas Brox19,20 &
- …
- Olaf Ronneberger19,20
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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Authors and Affiliations
Lehrstuhl für Mustererkennung und Bildverabeitung, Institut für Informatik, Germany
Dominic Mai, Philipp Fischer, Thomas Brox & Olaf Ronneberger
BIOSS Centre of Biological Signalling Studies, Germany
Dominic Mai, Klaus Palme, Thomas Brox & Olaf Ronneberger
Institut für Biologie II, Albert-Ludwigs-Universität Freiburg, Germany
Jasmin Dürr & Klaus Palme
INRA Versailles, France
Thomas Blein
- Dominic Mai
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- Philipp Fischer
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- Thomas Blein
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- Jasmin Dürr
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- Klaus Palme
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- Thomas Brox
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- Olaf Ronneberger
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Editors and Affiliations
Faculty of Mathematics and Computer Science, Saarland University, Campus E1.7, 66041, Saarbrücken, Germany
Joachim Weickert
Faculty of Mathematics and Computer Science, Saarland University, Campus E1.3, 66041, Saarbrücken, Germany
Matthias Hein
Computer Vision and Multimodal Computing, Max-Planck-Institute for Informatics, Campus E 1.4, 66123, Saarbrücken, Germany
Bernt Schiele
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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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