Ahmed J. Afifi

Orcid: 0000-0001-6782-6753

Affiliations:
  • Technical University of Berlin, Germany (PhD 2021)


According to our database1, Ahmed J. Afifi authored at least 21 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Improving Mineral Classification Using Multimodal Hyperspectral Point Cloud Data and Multi-Stream Neural Network.
Remote. Sens., July, 2024

Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences.
IEEE Trans. Geosci. Remote. Sens., 2024

Dimensional Dilemma: Navigating the Fusion of Hyperspectral and Lidar Point Cloud Data for Optimal Precision - 2D vs. 3D.
Proceedings of the IGARSS 2024, 2024

Towards 3D Hyperspectral Imaging.
Proceedings of the IGARSS 2024, 2024

2023
Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences.
CoRR, 2023

Dual-Branch U-Net Architecture for Retinal Lesions Segmentation on Fundus Image.
IEEE Access, 2023

Transformer-Based Models for Hyperspectral Point Clouds Segmentation.
Proceedings of the 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, 2023

2022
Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research.
Artif. Intell. Rev., 2022

2021
Geometric and semantic understanding of objects from a single image using deep learning.
PhD thesis, 2021

Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere.
IEEE Access, 2021

Synthesizing Fundus Photographies for Training Segmentation Networks.
Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, 2021

2020
Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN.
Proceedings of the 15th International Joint Conference on Computer Vision, 2020

2019
Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation.
Expert Syst. Appl., 2019

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review.
IEEE Access, 2019

Impact of Image Enhancement Technique on CNN Model for Retinal Blood Vessels Segmentation.
IEEE Access, 2019

2018
Simultaneous Object Classification and Viewpoint Estimation using Deep Multi-task Convolutional Neural Network.
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018), 2018

Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss.
Proceedings of the 2018 Digital Image Computing: Techniques and Applications, 2018

Retinal Blood Vessels Extraction of Challenging Images.
Proceedings of the Data Mining - 16th Australasian Conference, AusDM 2018, Bahrurst, NSW, 2018

2017
Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network.
Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017

2016
Object Depth Estimation from a Single Image Using Fully Convolutional Neural Network.
Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications, 2016

2012
Content-Based Image Retrieval Using Invariant Color and Texture Features.
Proceedings of the 2012 International Conference on Digital Image Computing Techniques and Applications, 2012


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