4 papers
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
Galadrielle Humblot-Renaux, Vasiliki Ismiroglou, Malte Pedersen
Label uncertainty and annotator disagreement are common challenges in the field of computer vision, yet their study has largely been confined to the medical domain or to generic im…
Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
Vasiliki Ismiroglou, Stefan H. Bengtson, Tasos Benos +2
Visibility in underwater environments degrades rapidly under turbid conditions, yet the effects on computer-vision models remain unclear. This issue is compounded by reliance on sy…
Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
Vasiliki Ismiroglou, Malte Pedersen, Stefan H. Bengtson +2
In recent years, the underwater image formation model has found extensive use in the generation of synthetic underwater data. Although many approaches focus on scenes primarily aff…
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish
Stefan Hein Bengtson, Daniel Lehotský, Vasiliki Ismiroglou +3
Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. I…