activity
20192022
most citedA Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis

176 citations · 288 across the 6 of their papers we have counts for

collaborators
Showing 2022Show all

5 papers · 1 filter

cs.CV2022★ 93 cited

Adaptive deep learning framework for robust unsupervised underwater image enhancement

Alzayat Saleh, Marcus Sheaves, Dean Jerry +1

One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data. Underwater images are difficult to capture…

cs.CV2022★ 8 cited

How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach

Alzayat Saleh, Marcus Sheaves, Dean Jerry +1

Tracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how the…

cs.CV2022★ 4 cited

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

Alzayat Saleh, Marcus Sheaves, Dean Jerry +1

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their…

cs.CV2022★ 5 cited

Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach

Alzayat Saleh, Marcus Sheaves, Dean Jerry +1

Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manua…

cs.CV2022★ 2 cited

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi

Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change,…