Our work crosses many domains including the development of novel machine/ deep learning methodology, solution techniques, pose estimation, medical imaging and forensic identification.
Biometrics and Forensic Identification
Our work focuses on the development of computer vision techniques for the forensic identification of individuals and criminal evidence, primary through photo and video. This multidisciplinary work includes identification through and the study of anatomical variability, age estimation, scene comparison, localisation and transcription of text.
H-Unique
Large Scale Identification in Natural Scenes
Optical Character Recognition
Medical Imaging
Our work in medical imaging focuses on the development of computer vision techniques to automatically analyse and interpret medical images, including tomography and pre-clinical imaging. We have a strong focus on pathology extraction and measurement, diagnosis and technology development in ophthalmic imaging, cancer and COVID-19.
Glaucoma Diagnosis
Breast Cancer
COVID-19 Prognosis
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs in order to take actions or make recommendations based on that information. Much of our work is centred on the development of computer vision methodology in diverse areas, including pose estimation, pharmaceutical manufacturing, and environmental science.
Pose Estimation
Pharmaceutical Manufacturing
Environmental Science
Machine Learning
Machine learning (ML) is a subfield of AI that is devoted to understanding and building methods that are capable of learning, i.e. they can leverage data to improve performance on a task or set of tasks. Our work in machine learning focuses on algorithm development, efficient methodology, and numerical solution techniques.
Deep Learning
Image Segmentation
Classification
Publications
2022
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Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning
Zhang Z, Jiang R, Zhang C, Williams B, Jiang Z, Li CT, Chazot P, Pavese N, Bouridane A, Beghdadi A. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022.
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Graph-context Attention Networks for Size-varied Deep Graph Matching
Jiang Z, Rahmani H, Angelov P, Black S, Williams BM. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (pp. 2343-2352).
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Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning
Baisa NL, Williams B, Rahmani H, Angelov P, Black S. In 2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) 2022 Apr 19 (pp. 1-6). IEEE.
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Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review
Coan L, Williams B, Venkatesh KA, Upadhyaya S, Czanner S, Venkatesh R, Willoughby CE, Kavitha S, Czanner G. arXiv preprint arXiv:2204.05591. 2022 Apr 12.
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Optimising Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network
Li X, Williams B, May RK, Zhong S, Evans MJ, Gladden LF, Zeitler JA, Lin H. IEEE Transactions on Terahertz Science and Technology. 2022 Apr 1.
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Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization
Vyas R, Williams BM, Rahmani H, Boswell-Challand R, Jiang Z, Angelov P, and Black S. Sensors. 2022; 22(4): 1569.
2021
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Recent Advances of Continual Learning in Computer Vision: An Overview
Qu H, Rahmani H, Xu L, Williams B, Liu J. arXiv preprint arXiv:2109.11369. 2021.
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Multi-Branch with Attention Network for Hand-Based Person Recognition
Baisa NL, Williams B, Rahmani H, Angelov P, Black S. arXiv preprint arXiv:2108.02234. 2021.
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Robust End-to-End Hand Identification via Holistic Multi-Unit Knuckle Recognition
Vyas R, Rahmani H, Boswell-Challand R, Angelov P, Black S, Williams B. In International Joint Conference on Biometrics (IJCB) 2021. IEEE.
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EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection
Krishna Adithya V, Williams BM, Czanner S, Kavitha S, Friedman DS, Willoughby CE, Venkatesh R, Czanner G. Journal of Imaging. 2021 Jun;7(6):92.
2020
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Simultaneous optical coherence tomography and Scheimpflug imaging using the same incident light
Li X, Lawman S, Williams BM, Ye S, Shen Y, Zheng Y. Optics Express. 2020;28(26):39660-76.
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En-face analysis of the human limbal lymphatic vasculature
Palme C, Ahmad S, Romano V, Seifarth C, Williams B, Parekh M, Kaye SB, Steger B. Experimental Eye Research. 2020;201:108278.
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Studying pharmaceutical tablets mixing process inside a perforated pan-coater using in-line terahertz sensing
Li X, Bawuah P, Williams BM, Zeitler JA, Lin H. In IRMMW-THz 2020 (pp. 01-02). IEEE.
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Observing liquid water build-up in proton exchange membrane fuel cells using terahertz imaging and high-resolution optical gauging
Alves-Lima DF, Schlegl H, Williams BM, Letizia R, Dawson R, Lin H. In IRMMW-THz 2020 (pp. 1-2). IEEE.
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A deep convolutional neural network-based approach for superficial dorsal hand vein pattern matching
Gu X, Williams B, Black S. In EAB-RPC 2020.
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H-Unique-In search of uniqueness-harnessing anatomical hand variation
Williams B, Gu X, Black S. In EAB-RPC 2020.
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Fast Discrete Homotopy Solution Method for Two Problems in Image Deconvolution
Williams BM, Ghanbari B, Chen K, Rada L. In International Online Conference on Intelligent Decision Science 2020 (pp. 583-594). Springer, Cham.
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An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study
Williams BM, Borroni D, Liu R, Zhao Y, Zhang J, Lim J, Ma B, Romano V, Qi H, Ferdousi M, Petropoulos IN. Diabetologia. 2020;63(2):419-30.
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Medical Image Understanding and Analysis: 23rd Conference
Zheng Y, Williams BM, Chen K, editors. MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. Springer Nature; 2020.
2019
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Improving fetal head contour detection by object localisation with deep learning
Al-Bander B, Alzahrani T, Alzahrani S, Williams BM, Zheng Y. In MIUA 2019 (pp. 142-150). Springer, Cham.
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Learning active contour models for medical image segmentation
Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 (pp. 11632-11640).