activity
20192023
most citedAdaptive deep learning framework for robust unsupervised underwater image enhancement

93 citations · 111 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization

Alzayat Saleh, Md Mehedi Hasan, Herman W Raadsma +3

Accurate weight estimation and morphometric analyses are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breedin…

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★ 1 cited

A lightweight Transformer-based model for fish landmark detection

Alzayat Saleh, David Jones, Dean Jerry +1

Transformer-based models, such as the Vision Transformer (ViT), can outperform onvolutional Neural Networks (CNNs) in some vision tasks when there is sufficient training data. Howe…

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.CV2019

Automatic Weight Estimation of Harvested Fish from Images

Dmitry A. Konovalov, Alzayat Saleh, Dina B. Efremova +2

Approximately 2,500 weights and corresponding images of harvested Lates calcarifer (Asian seabass or barramundi) were collected at three different locations in Queensland, Australi…