A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video
- DOI
- 10.3390/s23063311
- Published
- 2023-03-21
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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BibTeX
@article{allodium:10.3390/s23063311,
title = {A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video},
author = {Gordon Böer and Joachim Paul Gröger and Sabah Badri-Höher and Boris Cisewski and Helge Renkewitz and Felix Mittermayer and Tobias Strickmann and Hauke Schramm},
year = {2023},
journal = {Sensors},
doi = {10.3390/s23063311},
url = {https://doi.org/10.3390/s23063311}
}RIS
TY - JOUR TI - A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video AU - Gordon Böer AU - Joachim Paul Gröger AU - Sabah Badri-Höher AU - Boris Cisewski AU - Helge Renkewitz AU - Felix Mittermayer AU - Tobias Strickmann AU - Hauke Schramm PY - 2023 JO - Sensors DO - 10.3390/s23063311 UR - https://doi.org/10.3390/s23063311 ER -
APA
Böer, G., Gröger, J. P., Badri-Höher, S., Cisewski, B., Renkewitz, H., Mittermayer, F., Strickmann, T., & Schramm, H. (2023). A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video. Sensors. https://doi.org/10.3390/s23063311
Source records
- crossref · retrieved 2026-09-25T01:20:36.400Z