Looking for Seagrass: Deep Learning for Visual Coverage Estimation (CROSBI ID 712305)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Reus, Gereon ; Möller, Thomas ; Jäger, Jonas ; Schultz, Stewart T. ; Kruschel, Claudia ; Hasenauer, Julian ; Wolff, Viviane ; Fricke- Neuderth, Klaus
engleski
Looking for Seagrass: Deep Learning for Visual Coverage Estimation
Underwater videography enables marine researchers to collect enormous amounts of seagrass image data. This collection is fast and cheap but the manual analysis of such data is slow and expensive. Therefore, we propose a machine- learning approach for the automatic seagrass coverage estimation of the sea bottom. Our contribution is the investigation of CNN features to describe patches and superpixels of seagrass. CNN features are the activations of a specific layer in a deep convolutional neural network. We also provide the first public available dataset of seagrass images that can be used as a benchmark for automatic seagrass segmentation. Our best method achieves an accuracy of 94.5% for seagrass segmentation on the provided dataset. Our code and dataset is available on GitHub: https://enviewfulda.github.io/LookingForSeagrass
feature extraction ; training ; convolutional neural networks ; image segmentation ; logistics ; clustering algorithms ; measurement
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Podaci o prilogu
1-6.
2018.
objavljeno
10.1109/OCEANSKOBE.2018.8559302
Podaci o matičnoj publikaciji
Proceedings 2018 OCEANS - MTS/IEEE Kobe Techno- Oceans (OTO)
Kobe: Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-1655-0
Podaci o skupu
OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO 2018))
predavanje
28.05.2018-31.05.2018
Kobe, Japan