Abstract
In this paper, we aim for detection and segmentation of Arabidopsis 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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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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DOI: https://doi.org/10.1007/978-3-642-40602-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40601-0
Online ISBN: 978-3-642-40602-7
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