Böer, G.; Gröger, J.P.; Badri-Höher, S.; Cisewski, B.; Renkewitz, H.; Mittermayer, F.; Strickmann, T.; Schramm, H.
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 2023, 23, 3311.
https://doi.org/10.3390/s23063311
AMA Style
Böer G, Gröger JP, Badri-Höher S, Cisewski B, Renkewitz H, Mittermayer F, Strickmann T, Schramm H.
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. 2023; 23(6):3311.
https://doi.org/10.3390/s23063311
Chicago/Turabian Style
Böer, Gordon, Joachim Paul Gröger, Sabah Badri-Höher, Boris Cisewski, Helge Renkewitz, Felix Mittermayer, Tobias Strickmann, and Hauke Schramm.
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 23, no. 6: 3311.
https://doi.org/10.3390/s23063311
APA Style
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, 23(6), 3311.
https://doi.org/10.3390/s23063311
