Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Alzayat"'
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely either on discriminative models, which struggle with transparent or reflect
Externí odkaz:
http://arxiv.org/abs/2410.02072
Autor:
Issam H. Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting productivity in marine and aquaculture applications. Some methods are based on manual collection of these meas
Externí odkaz:
https://doaj.org/article/9de071b756e24d92ac88cac60a6b6108
Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures. Automatic weed detection using computer vision and deep learning offers a promising solution. However, conventional deep learning metho
Externí odkaz:
http://arxiv.org/abs/2405.07399
Shadows significantly impact computer vision tasks, particularly in outdoor environments. State-of-the-art shadow removal methods are typically too computationally intensive for real-time image processing on edge hardware. We propose ShadowRemovalNet
Externí odkaz:
http://arxiv.org/abs/2403.08142
Autor:
Azghadi, Mostafa Rahimi, Olsen, Alex, Wood, Jake, Saleh, Alzayat, Calvert, Brendan, Granshaw, Terry, Fillols, Emilie, Philippa, Bronson
Precise robotic weed control plays an essential role in precision agriculture. It can help significantly reduce the environmental impact of herbicides while reducing weed management costs for farmers. In this paper, we demonstrate that a custom-desig
Externí odkaz:
http://arxiv.org/abs/2401.13931
Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hinder the development of deep learning models for generalizable weed ide
Externí odkaz:
http://arxiv.org/abs/2310.12465
Autor:
Saleh, Alzayat, Hasan, Md Mehedi, Raadsma, Herman W, Khatkar, Mehar S, Jerry, Dean R, Azghadi, Mostafa Rahimi
Accurate weight estimation and morphometric analyses are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production a
Externí odkaz:
http://arxiv.org/abs/2307.07732
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 and are often of poor quality due to the distortion and loss of colou
Externí odkaz:
http://arxiv.org/abs/2212.08983
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. However, (CNNs) have a strong and useful inductive bias for vision tasks (
Externí odkaz:
http://arxiv.org/abs/2209.05777
DNN for fish tracking and segmentation based on high-quality labels is expensive. Alternative unsupervised approaches rely on spatial and temporal variations that naturally occur in video data to generate noisy pseudo-ground-truth labels. These pseud
Externí odkaz:
http://arxiv.org/abs/2208.10662