Abstract
We design a two stage image segmentation method, comprising a distance transform estimating neural network and watershed segmentation. It allows segmentation and tracking of colliding objects without any assumptions on object behavior or global object appearance as the proposed machine learning step is trained on contour information only. Our method is also capable of segmenting partially vanishing contact surfaces of visually merged objects. The evaluation is performed on a dataset of collisions of Drosophila melanogaster larvae manually labeled with pixel accuracy. The proposed pipeline needs no manual parameter tuning and operates at high frame rates. We provide a detailed evaluation of the neural network design including 1200 trained networks.
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Notes
- 1.
Please note that reasonable small values (\(1 < \sigma \le 7\)) lead to comparable results.
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Klemm, S., Jiang, X., Risse, B. (2019). Deep Distance Transform to Segment Visually Indistinguishable Merged Objects. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_29
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DOI: https://doi.org/10.1007/978-3-030-12939-2_29
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