collaborators

10 papers

cs.CV2026

How many labels do you need? A decision framework for cross-habitat marine species recognition

Alzayat Saleh, Mostafa Rahimi Azghadi

Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort r…

cs.LG2026

Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks

Alzayat Saleh, Mostafa Rahimi Azghadi

Satellite sea surface temperature (SST) products underpin global coral bleaching monitoring, yet they measure only the ocean skin. Corals inhabit depths from the shallows to beyond…

cs.CV2026

Multi-label Instance-level Generalised Visual Grounding in Agriculture

Mohammadreza Haghighat, Alzayat Saleh, Mostafa Rahimi Azghadi

Understanding field imagery such as detecting plants and distinguishing individual crop and weed instances is a central challenge in precision agriculture. Despite progress in visi…

cs.CV2025

Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity

Alzayat Saleh, Shunsuke Hatano, Mostafa Rahimi Azghadi

The automated management of invasive weeds is critical for sustainable agriculture, yet the performance of deep learning models in real-world fields is often compromised by two fac…

cs.CV2025

A Practical Approach to Underwater Depth and Surface Normals Estimation

Alzayat Saleh, Melanie Olsen, Bouchra Senadji +1

Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely eithe…

cs.CV2025

FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics

Alzayat Saleh, Alex Olsen, Jake Wood +2

Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and locali…