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Pamela Johnston
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Books and Theses
- 2019
- [b1]Pamela Johnston:
Beyond the pixels: learning and utilising video compression features for localisation of digital tampering. Robert Gordon University, Aberdeen, UK, 2019
Journal Articles
- 2024
- [j6]Sophie Crawford Haynes, Pamela Johnston, Eyad Elyan:
Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. Multim. Tools Appl. 83(31): 76753-76772 (2024) - 2022
- [j5]Faseela Abdullakutty, Eyad Elyan, Pamela Johnston, Adamu Ali-Gombe:
Deep Transfer Learning on the Aggregated Dataset for Face Presentation Attack Detection. Cogn. Comput. 14(6): 2223-2233 (2022) - [j4]Faseela Abdullakutty, Pamela Johnston, Eyad Elyan:
Fusion Methods for Face Presentation Attack Detection. Sensors 22(14): 5196 (2022) - 2021
- [j3]Faseela Abdullakutty, Eyad Elyan, Pamela Johnston:
A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods. Inf. Fusion 75: 55-69 (2021) - 2020
- [j2]Pamela Johnston, Eyad Elyan, Chrisina Jayne:
Video tampering localisation using features learned from authentic content. Neural Comput. Appl. 32(16): 12243-12257 (2020) - 2019
- [j1]Pamela Johnston, Eyad Elyan:
A review of digital video tampering: From simple editing to full synthesis. Digit. Investig. 29: 67-81 (2019)
Conference and Workshop Papers
- 2023
- [c7]Pamela Johnston, Mark Zarb, Carlos Francisco Moreno-García:
Student Interaction with a Virtual Learning Environment: An Empirical Study of Online Engagement Behaviours During and Since the Time of COVID-19. FIE 2023: 1-8 - [c6]Faseela Abdullakutty, Eyad Elyan, Pamela Johnston:
Unmasking the Imposters: Task-specific feature learning for face presentation attack detection. IJCNN 2023: 1-8 - 2021
- [c5]Faseela Abdullakutty, Eyad Elyan, Pamela Johnston:
Face Spoof Detection: An Experimental Framework. EANN 2021: 293-304 - 2020
- [c4]Eyad Elyan, Carlos Francisco Moreno-García, Pamela Johnston:
Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks. EANN 2020: 215-224 - [c3]Carlos Francisco Moreno-García, Pamela Johnston, Bello Garkuwa:
Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. IJCNN 2020: 1-7 - 2018
- [c2]Pamela Johnston, Eyad Elyan, Chrisina Jayne:
Toward Video Tampering Exposure: Inferring Compression Parameters from Pixels. EANN 2018: 44-57 - [c1]Pamela Johnston, Eyad Elyan, Chrisina Jayne:
Spatial Effects of Video Compression on Classification in Convolutional Neural Networks. IJCNN 2018: 1-8
Coauthor Index
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last updated on 2024-12-10 21:49 CET by the dblp team
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