Prof David Windridge
| Name | Prof David Windridge |
|---|---|
| Job title | Professor of Data Science and Machine Learning |
| Research institute | |
| Primary appointment | Computer Science |
| Email address | d.windridge@mdx.ac.uk |
| ORCID | https://orcid.org/0000-0001-5507-8516 |
| Contact category | Academic staff |
Biography
Biography Professor of Data Science & Machine Learning, Dept. of Computing, Head of AI and Machine Learning Group Associate Professor, Dept. of Computing, Middlesex University . Senior Research Fellow/Research Fellow, University of Surrey (Centre for Vision Speech & Signal Processing, CVSSP) PhD Bristol University (Cosmology/Astrophysics) [EPSRC ROPA funded scholarship]
Teaching I am the originator & Programme Lead for the Data Science M.Sc. Programme. I also lead the Foundations of Machine Learning Module CST4050, and co-lead the Business Informatics Module CST2321.
Supervision
| Doctoral Researcher | Award | Status | Role | |
|---|---|---|---|---|
| Waseemah Moedeen | PhD | Enrolled Student | Supervisor | Jan 2025 - Present |
| Faryal Batool | PhD | Enrolled Student | Supervisor | Jan 2023 - Present |
Education and qualifications
Grants
Projects
- Judicial Decision Data Gathering, Encoding and Sharing
Prizes and Awards
External activities
Editorial Board Springer Quantum Machine Intelligence
External committee
Member of Editorial Board
Springer Quantum Machine Intelligence (Impact Factor 5.1)
https://link.springer.com/journal/42484
01 Jan 2019
Search amongst my papers with the word 'quantum' in the abstract on Google Scholar.
Steering Board of Multi-disciplinary Trends in Artificial Intelligence Conference (MIWAI)
External committee
Springer
MIWAI is currently in its 17th year and is the foremost conference for interdisciplinarity in AI.
I will act as host for the 18th MIWAI Conference to be held at Middlesex University in 2025.
Search amongst my papers with the word 'kernel', 'fusion', or 'classifier combination' in the abstract on Google Scholar.
Research outputs
Margin-aware active learning for user-adaptive text classification
Tirunagari, S., Dhami, M. and Windridge, D. 2026. Margin-aware active learning for user-adaptive text classification. 9th International Conference on Natural Language Processing and Information Retrieval. Kyushu University, Fukuoka, Japan 12 - 14 Dec 2025 Springer.Kernel methods in the age of deep learning: from multimodal fusion to quantum machine learning
Windridge, D. 2025. Kernel methods in the age of deep learning: from multimodal fusion to quantum machine learning. 18th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Ho Chi Minh City, Vietnam 03 - 05 Dec 2025The application of pre-trained transformer models to UK Court of Appeal legal judgments
Abbas, W., Zia, T., Tirunagari, S., Chennareddy, V., Dhami, M. and Windridge, D. 2025. The application of pre-trained transformer models to UK Court of Appeal legal judgments. Quan, T.T., Sombattheera, C., Pham, H.-A. and Tran, N.T. (ed.) 18th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Ho Chi Minh City, Vietnam 03 - 05 Dec 2025 Singapore Springer. pp. 252-264 https://doi.org/10.1007/978-981-95-4963-4_21Judges on trial: the future of research on judicial decision-making
Dhami, M., Kajdanowicz, T., Windridge, D. and Boukacem-Zeghmouri, C. 2026. Judges on trial: the future of research on judicial decision-making. in: Shortland, N. and Alison, L. (ed.) Big Ideas in Forensic Psychology: Visions of a Forensic Future From Leading Voices in the Field Wiley.EEL-GA: an evolutionary clustering framework for energy-efficient 3D wireless sensor networks in smart forestry
Batool, F., Ali, K., Lasebae, A., Windridge, D. and Kiyani, A. 2025. EEL-GA: an evolutionary clustering framework for energy-efficient 3D wireless sensor networks in smart forestry. Sensors. 25 (17). https://doi.org/10.3390/s25175250Identifying social vulnerability profiles for coastal flood using supervised and unsupervised machine learning: a case study of Lekki peninsula, Lagos, Nigeria
Akindejoye, A., Viavattene, C., Priest, S. and Windridge, D. 2025. Identifying social vulnerability profiles for coastal flood using supervised and unsupervised machine learning: a case study of Lekki peninsula, Lagos, Nigeria. International Journal of Disaster Risk Reduction. 127. https://doi.org/10.1016/j.ijdrr.2025.105693Classical to quantum knowledge distillation: a study on the impact of hybridization
Piperno, S., Vittori, G., Windridge, D., Rosato, A. and Panella, M. 2025. Classical to quantum knowledge distillation: a study on the impact of hybridization. 2025 International Joint Conference on Neural Networks. Rome, Italy 30 Jun - 05 Jul 2025 IEEE. https://doi.org/10.1109/ijcnn64981.2025.11227730A latent diffusion approach to visual attribution in medical imaging
Siddiqui, A.A., Tirunagari, S., Zia, T. and Windridge, D. 2025. A latent diffusion approach to visual attribution in medical imaging. Scientific Reports. 15 (1). https://doi.org/10.1038/s41598-024-81646-xQuantum enhanced knowledge distillation
Simone, P., Lavagna, L., De Falco, F,, Ceschini, A., Rosato, A., Windridge, D. and Panella, M. 2024. Quantum enhanced knowledge distillation. Quantum Techniques in Machine Learning 2024 Conference. Melbourne, Australia 24 - 29 Nov 2024Accelerating material discovery for CdTe solar cells using knowledge intense word embeddings
Liu, X., Barth, K., Windridge, D. and Xu, K. 2024. Accelerating material discovery for CdTe solar cells using knowledge intense word embeddings. 52nd Photovoltaic Specialist Conference. Seattle, WA, USA 09 - 14 Jun 2024 IEEE. pp. 0281-0283 https://doi.org/10.1109/pvsc57443.2024.10749288Faithful Counterfactual Visual Explanations (FCVE)
Khan, B., Tariq, S., Zia, T., Ahsan, M. and Windridge, D. 2024. Faithful Counterfactual Visual Explanations (FCVE). Knowledge-Based Systems. 294. https://doi.org/10.1016/j.knosys.2024.111668An evolutionary approach to automated class-specific data augmentation for image classification
Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12A topological features based quantum kernel
Incudini, M., Martini, F., Di Pierro, A. and Windridge, D. 2023. A topological features based quantum kernel. 7th International Conference on Quantum Techniques in Machine Learning. CERN, Geneva 19 - 24 Nov 2023Resource saving via ensemble techniques for quantum neural networks
Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. Quantum Machine Intelligence. 5 (2). https://doi.org/10.1007/s42484-023-00126-zVisual attribution using Adversarial Latent Transformations
Zia, T., Wahab, A., Windridge, D., Tirunagari, S. and Bhatti, N. 2023. Visual attribution using Adversarial Latent Transformations. Computers in Biology and Medicine. 166. https://doi.org/10.1016/j.compbiomed.2023.107521Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures
Towakel, P., Windridge, D. and Nguyen, H. 2023. Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures. IEEE Transactions on Instrumentation and Measurement. 72, pp. 1-15. https://doi.org/10.1109/TIM.2023.3307768Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability
Balla, Y., Tirunagari, S. and Windridge, D. 2023. Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability. Indian Pediatrics. 60 (7), pp. 561-569. https://doi.org/10.1007/s13312-023-2936-8Interpretable chronic kidney disease risk prediction from clinical data using machine learning
Chennareddy, V., Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Interpretable chronic kidney disease risk prediction from clinical data using machine learning. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_63Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings
Morusupalli, R., Dandibhotla, T., Atluri, V., Windridge, D., Lingras, P. and Komati, V. (ed.) 2023. Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings. Springer.Addressing challenges in healthcare big data analytics
Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Addressing challenges in healthcare big data analytics. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_70The quantum path kernel: a generalized neural tangent kernel for deep quantum machine learning
Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2023. The quantum path kernel: a generalized neural tangent kernel for deep quantum machine learning. IEEE Transactions on Quantum Engineering. 4. https://doi.org/10.1109/TQE.2023.3287736Special issue on quantum inspired neural networks for engineering optimization
Pandey, H.M., Layeb, A. and Windridge, D. 2023. Special issue on quantum inspired neural networks for engineering optimization. Neural Processing Letters. 55 (4), pp. 3811-3812. https://doi.org/10.1007/s11063-023-11298-xResource saving via ensemble techniques for quantum neural networks
Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. https://doi.org/10.48550/arXiv.2303.11283The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning
Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2022. The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning. https://doi.org/10.48550/arXiv.2212.11826Discriminator-based adversarial networks for knowledge graph completion
Tubaishat, A., Zia, T., Faiz, R., Al Obediat, F., Shah, B. and Windridge, D. 2022. Discriminator-based adversarial networks for knowledge graph completion. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07680-wA user-guided personalization methodology to facilitate new smart home occupancy
Ali, S.M.M., Augusto, J., Windridge, D. and Ward, E. 2023. A user-guided personalization methodology to facilitate new smart home occupancy. Universal Access in the Information Society. 22 (3), pp. 869-891. https://doi.org/10.1007/s10209-022-00883-xReducing the dependency of having prior domain knowledge for effective online information retrieval
Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2023. Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems. 40 (4). https://doi.org/10.1111/exsy.13014A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk
Nwegbu, N., Tirunagari, S. and Windridge, D. 2022. A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports. 12 (1), pp. 1-16. https://doi.org/10.1038/s41598-022-08757-1VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging
Zia, T., Murtaza, S., Bhatti, N., Windridge, D. and Nisar, Z. 2022. VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging. Pattern Recognition Letters. 156, pp. 112-118. https://doi.org/10.1016/j.patrec.2022.02.005A generative adversarial network for single and multi-hop distributional knowledge base completion
Zia, T. and Windridge, D. 2021. A generative adversarial network for single and multi-hop distributional knowledge base completion. Neurocomputing. 461, pp. 543-551. https://doi.org/10.1016/j.neucom.2021.04.128Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
Navidan, H., Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks. 194, pp. 1-21. https://doi.org/10.1016/j.comnet.2021.108149Multi-view convolutional recurrent neural networks for lung cancer nodule identification
Naeem Abid, M., Zia, T., Ghafoor, M. and Windridge, D. 2021. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing. 453, pp. 299-311. https://doi.org/10.1016/j.neucom.2020.06.144Exposing students to new terminologies while collecting browsing search data (best technical paper)
Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2020. Exposing students to new terminologies while collecting browsing search data (best technical paper). 40th SGAI International Conference on Artificial Intelligence, AI 2020. Cambridge, UK 15 - 17 Dec 2020 Cham Springer. https://doi.org/10.1007/978-3-030-63799-6_1A genetic deep learning model for electrophysiological soft robotics
Pandey, H. and Windridge, D. 2021. A genetic deep learning model for electrophysiological soft robotics. Balas, V., Jain, L., Balas, M. and Shahbazova, S. (ed.) 8th International Workshop on Soft Computing Applications. Arad, Romania 13 - 15 Sep 2018 Cham Springer. https://doi.org/10.1007/978-3-030-51992-6_12A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems
Sazdar, A.M., Ghorashi, S.A., Moghtadaiee, V., Khonsari, A. and Windridge, D. 2020. A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems. Journal of Information Security and Applications. 53. https://doi.org/10.1016/j.jisa.2020.102515Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems
Pandey, H., Bessis, N., Das, S., Windridge, D. and Chaudhary, A. 2020. Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems. Neural Computing and Applications. 32 (12), pp. 7743-7745. https://doi.org/10.1007/s00521-020-04903-wEpileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns
Ramanna, S., Tirunagari, S. and Windridge, D. 2020. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. Health and Technology. 10 (3), pp. 699-709. https://doi.org/10.1007/s12553-019-00395-4On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination
Windridge, D., Svensson, H. and Thill, S. 2021. On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination. Adaptive Behavior. 29 (3), pp. 267-280. https://doi.org/10.1177/1059712319896489A generative adversarial strategy for modeling relation paths in knowledge base representation learning
Zia, T., Zahid, U. and Windridge, D. 2019. A generative adversarial strategy for modeling relation paths in knowledge base representation learning. KR2ML - Knowledge Representation and Reasoning Meets Machine Learning Workshop, NeurIPS 2019, Thirty-third Conference on Neural Information Processing Systems. Vancouver, Canada 09 - 14 Dec 2019Improving the adaptation process for a new smart home user
Ali, S., Augusto, J. and Windridge, D. 2019. Improving the adaptation process for a new smart home user. Bramer, M. and Petridis, M. (ed.) AI-2019 Thirty-ninth SGAI International Conference on Artificial Intelligence. Cambridge, United Kingdom 17 - 19 Dec 2019 Springer. https://doi.org/10.1007/978-3-030-34885-4_32Revisiting direct neuralisation of first-order logic
Gunn, I. and Windridge, D. 2018. Revisiting direct neuralisation of first-order logic. NeSy 2018 : Thirteenth International Workshop on Neural-Symbolic Learning and Reasoning. Prague, Czech Republic 23 - 24 Aug 2018Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI
Spiteri, M., Guillemaut, J., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2020. Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI. Neuroinformatics. 18 (1), pp. 151-162. https://doi.org/10.1007/s12021-019-09427-wAn approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning
Attfield, S., Fields, B., Windridge, D. and Xu, K. 2019. An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning. Conrad, J., Pickens, J., Jones, A., Baron, J. and Henseler, H. (ed.) First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019).. Montreal, Canada 17 Jun 2019 CEUR Workshop Proceedings. pp. 7-11Automatic delineation of kidney region in DCE-MRI
Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2019. Automatic delineation of kidney region in DCE-MRI.A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Ali, M., Augusto, J. and Windridge, D. 2019. A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation. Applied Artificial Intelligence. 33 (8), pp. 747-774. https://doi.org/10.1080/08839514.2019.1603784A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration
Brown, M., Windridge, D. and Guillemaut, J. 2019. A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration. Pattern Recognition. 93, pp. 36-54. https://doi.org/10.1016/j.patcog.2019.04.002Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics
Windridge, D. and Kammueller, F. 2020. Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics. FICC 2019: Future of Information and Communications Conference. San Francisco, USA 14 - 15 Mar 2019 Springer. pp. 271-283 https://doi.org/10.1007/978-3-030-12385-7_22Data governance in the health industry: investigating data quality dimensions within a big data context
Juddoo, S., George, C., Duquenoy, P. and Windridge, D. 2018. Data governance in the health industry: investigating data quality dimensions within a big data context. Applied System Innovation. 1 (4), pp. 1-16. https://doi.org/10.3390/asi1040043A comprehensive classification of deep learning libraries
Pandey, H. and Windridge, D. 2019. A comprehensive classification of deep learning libraries. Yang, X.S., Sherratt, S., Dey, N. and Joshi, A. (ed.) 3rd International Congress on Information and Communication Technology. London, UK 27 - 28 Feb 2018 Singapore Springer. pp. 427-435 https://doi.org/10.1007/978-981-13-1165-9_40Quantum error-correcting output codes
Windridge, D., Mengoni, R. and Nagarajan, R. 2018. Quantum error-correcting output codes. International Journal of Quantum Information. 16 (8). https://doi.org/10.1142/S0219749918400038Representational fluidity in embodied (artificial) cognition
Windridge, D. and Thill, S. 2018. Representational fluidity in embodied (artificial) cognition. Biosystems. 172, pp. 9-17. https://doi.org/10.1016/j.biosystems.2018.07.007Postoperative paediatric cerebellar mutism syndrome- identifying quantitative biomarkers on longitudinal follow-up MRI
Avula, S., Spiteri, M., Guillemaut, J.-Y., Windridge, D., Kumar, R., Pizer, B., Mallucci, C. and Lewis, E. 2018. Postoperative paediatric cerebellar mutism syndrome- identifying quantitative biomarkers on longitudinal follow-up MRI. Neuro-Oncology. 20 (suppl_2), p. i171. https://doi.org/10.1093/neuonc/noy059.647An improved block matching algorithm for motion estimation in video sequences and application in robotics
Bhattacharjee, K., Kumar, S., Pandey, H., Pant, M., Windridge, D. and Chaudhary, A. 2018. An improved block matching algorithm for motion estimation in video sequences and application in robotics. Computers & Electrical Engineering. 68, pp. 92-106. https://doi.org/10.1016/j.compeleceng.2018.03.045Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease
Spanos, C., Maldonado, E.M., Fisher, C.P., Leenutaphong, P., Oviedo-Orta, E., Windridge, D., Salguero, F.J., Bermúdez-Fajardo, A., Weeks, M.E., Evans, C., Corfe, B.M., Rabbani, N., Thornalley, P.J., Miller, M.H., Wang, H., Dillon, J.F., Quaglia, A., Dhawan, A., Fitzpatrick, E. and Moore, J.B. 2018. Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease. Proteome Science. 16 (1). https://doi.org/10.1186/s12953-018-0131-yHamming distance kernelisation via topological quantum computation
Di Pierro, A., Mengoni, R., Nagarajan, R. and Windridge, D. 2017. Hamming distance kernelisation via topological quantum computation. Martín-Vide C., Neruda R. and Vega-Rodríguez M. (ed.) 6th International Conference on the Theory and Practice of Natural Computing. Prague, Czech Republic 18 - 20 Dec 2017 Springer. pp. 269-280 https://doi.org/10.1007/978-3-319-71069-3_21Exploiting dream-like simulation mechanisms to develop safer agents for automated driving: the "Dreams4Cars" EU research and innovation action
Da Lio, M., Mazzalai, A., Windridge, D., Thill, S., Svensson, H., Yueksel, M., Gurney, K., Saroldi, A., Andreone, L., Anderson, S.R. and Heich, H.-J. 2017. Exploiting dream-like simulation mechanisms to develop safer agents for automated driving: the "Dreams4Cars" EU research and innovation action. IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). Yokohama, Japan 16 - 19 Oct 2017 IEEE. pp. 1-6 https://doi.org/10.1109/ITSC.2017.8317649Emergent intentionality in perception-action subsumption hierarchies
Windridge, D. 2017. Emergent intentionality in perception-action subsumption hierarchies. Frontiers in Robotics and AI. 4. https://doi.org/10.3389/frobt.2017.00038Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition
Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2017. Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition. Machine Vision and Applications. 28 (3-4), pp. 393-407. https://doi.org/10.1007/s00138-017-0835-5Quantum Bootstrap Aggregation
Windridge, D. and Nagarajan, R. 2017. Quantum Bootstrap Aggregation. de Barros, J., Coecke, B. and Pothos, E. (ed.) Quantum Interaction. San Francisco, CA, USA 20 - 22 Jul 2016 Springer. pp. 115-121 https://doi.org/10.1007/978-3-319-52289-0_9A Hybrid Multi-Layer Architecture for Autonomous Vehicles Utilising a Hierarchical Perception-Action Dream Simulation Mechanism
Windridge, D., Gunn, I.A.D., Da Lio, M., Mazzalai, A., Thill, S., Svensson, H., Anderson, S., Blenkinsop, A., Gurney, K., James, S., Math, R., Berghoefer, E., Yueksel, M., Saroldi, A., Andreone, L. and Heich, H.-J. 2017. A Hybrid Multi-Layer Architecture for Autonomous Vehicles Utilising a Hierarchical Perception-Action Dream Simulation Mechanism. EUCognition 2017 Conference. Zürich, Switzerland 23 - 24 Nov 2017Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines
Attfield, S., Hewitt, D., Xu, K., Passmore, P., Wagstaff, A., Phillips, G., Windridge, D., Dash, G., Chapman, R. and Mason, L. 2016. Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines. IEEE VAST Challenge 2016. Baltimore, MD, USA 23 Oct 2016Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration
Avula, S., Spiteri, M., Kumar, R., Lewis, E., Harave, S., Windridge, D., Ong, C. and Pizer, B. 2016. Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration. Quantitative Imaging in Medicine and Surgery. 6 (5), pp. 535-544. https://doi.org/10.21037/qims.2016.10.11A generalised framework for saliency-based point feature detection
Brown, M., Windridge, D. and Guillemaut, J. 2017. A generalised framework for saliency-based point feature detection. Computer Vision and Image Understanding. 157, pp. 117-137. https://doi.org/10.1016/j.cviu.2016.09.008Can DMD obtain a scene background in color?
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2016. Can DMD obtain a scene background in color? 2016 International Conference on Image, Vision and Computing (ICIVC). Portsmouth, UK 03 - 05 Aug 2016 pp. 46-50 https://doi.org/10.1109/ICIVC.2016.7571272Criminal pattern identification based on modified K-means clustering
Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics (ICMLC). 2, pp. 799-806. https://doi.org/10.1109/ICMLC.2016.7872990Criminal pattern identification based on modified K-means clustering
Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics. Jeju, South Korea 10 - 13 Jul 2016 IEEE. pp. 799-806 https://doi.org/10.1109/ICMLC.2016.7872990Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT
Juneja, P., Evans, P., Windridge, D. and Harris, E. 2016. Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT. BMC Medical Imaging. 16 (1). https://doi.org/10.1186/s12880-016-0107-2Globally optimal 2D-3D registration from points or lines without correspondences
Brown, M., Windridge, D. and Guillemaut, J. 2015. Globally optimal 2D-3D registration from points or lines without correspondences. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile 07 - 13 Dec 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 2111-2119 https://doi.org/10.1109/ICCV.2015.244A generalisable framework for saliency-based line segment detection
Brown, M., Windridge, D. and Guillemaut, J. 2015. A generalisable framework for saliency-based line segment detection. Pattern Recognition. 48 (12), pp. 3993-4011. https://doi.org/10.1016/j.patcog.2015.06.015Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2015. Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. 2015 IEEE International Workshop on Information Forensics and Security (WIFS). Rome, Italy 16 - 19 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/WIFS.2015.7368599Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI
Spiteri, M., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2015. Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI. Journal of Medical Imaging. 2 (4), p. 044502. https://doi.org/10.1117/1.JMI.2.4.044502Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children
Spiteri, M., Lewis, E., Windridge, D. and Avula, S. 2015. Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children. Medical imaging 2015: Computer-Aided Diagnosis. Orlando, Florida, United States 21 Feb 2015 Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2081591A novel Markov logic rule induction strategy for characterizing sports video footage
Windridge, D., Kittler, J., De Campos, T., Yan, F., Christmas, W. and Khan, A. 2015. A novel Markov logic rule induction strategy for characterizing sports video footage. IEEE MultiMedia. 22 (2), pp. 24-35. https://doi.org/10.1109/MMUL.2014.36Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Hu, G. and Windridge, D. 2015. Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses. https://doi.org/10.48550/arXiv.1503.06316Breast cancer data analytics with missing values: a study on ethnic, age and income groups
Tirunagari, S., Poh, N., Abdulrahman, H., Nemmour, N. and Windridge, D. 2015. Breast cancer data analytics with missing values: a study on ethnic, age and income groups. ArXiv e-prints: Quantitative Biology > Quantitative Methods. https://doi.org/10.48550/arXiv.1503.03680Kernel combination via debiased object correspondence analysis
Windridge, D. and Yan, F. 2016. Kernel combination via debiased object correspondence analysis. Information Fusion. 27, pp. 228-239. https://doi.org/10.1016/j.inffus.2015.02.002On the intrinsic limits to representationally-adaptive machine-learning
Windridge, D. 2015. On the intrinsic limits to representationally-adaptive machine-learning. ArXiv e-prints.Detection of face spoofing using visual dynamics
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N. and Ho, A. 2015. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security. 10 (4), pp. 762-777. https://doi.org/10.1109/TIFS.2015.2406533Challenges in designing an online healthcare platform for personalised patient analytics
Poh, N., Tirunagari, S. and Windridge, D. 2014. Challenges in designing an online healthcare platform for personalised patient analytics. 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). Orlando, FL., USA 09 - 12 Dec 2014 IEEE. pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Aliabadi, K., Windridge, D. and Cooke, D. 2014. Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Orlando, FL., USA 09 - 12 Dec 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 304-309 https://doi.org/10.1109/CIDM.2014.7008682Automatic annotation of tennis games: an integration of audio, vision, and learning
Yan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q. 2014. Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing. 32 (11), pp. 896-903. https://doi.org/10.1016/j.imavis.2014.08.004Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation
Khan, A., Windridge, D. and Kittler, J. 2014. Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation. IEEE Transactions on Cybernetics. 44 (10), pp. 1910-1923. https://doi.org/10.1109/TCYB.2014.2299955Supervised selective kernel fusion for membrane protein prediction
Tatarchuk, A., Sulimova, V., Torshin, I., Mottl, V. and Windridge, D. 2014. Supervised selective kernel fusion for membrane protein prediction. Comin, M., Käll, L., Marchiori, E., Ngom, A. and Rajapakse, J. (ed.) 9th IAPR International Conference Pattern Recognition in Bioinformatics (PRIB 2014). Stockholm, Sweden 21 - 23 Aug 2014 Springer. pp. 98-109 https://doi.org/10.1007/978-3-319-09192-1_9Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs
Chernousova, E., Levdik, P., Tatarchuk, A., Mottl, V. and Windridge, D. 2014. Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs. 22nd International Conference on Pattern Recognition ICPR 2014. Stockholm, Sweden 24 - 28 Aug 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 3654-3659 https://doi.org/10.1109/ICPR.2014.628Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems
Da Lio, M., Biral, F., Bertolazzi, E., Galvani, M., Bosetti, P., Windridge, D., Saroldi, A. and Tango, F. 2015. Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems. IEEE Transactions on Intelligent Transportation Systems. 16 (1), pp. 244-263. https://doi.org/10.1109/TITS.2014.2330199A kernel-based framework for medical big-data analytics
Windridge, D. and Bober, M. 2014. A kernel-based framework for medical big-data analytics. in: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges Springer. pp. 197-208A saliency-based framework for 2D-3D registration
Brown, M., Guillemaut, J. and Windridge, D. 2014. A saliency-based framework for 2D-3D registration. 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014). Lisbon, Portugal 05 - 08 Jan 2014 SCITEPRESS - Science and Technology Publications. pp. 265-273 https://doi.org/10.5220/0004675402650273Domain anomaly detection in machine perception: a system architecture and taxonomy
Kittler, J., Christmas, W., De Campos, T., Windridge, D., Yan, F., Illingworth, J. and Osman, M. 2014. Domain anomaly detection in machine perception: a system architecture and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection
Chernousova, E., Razin, N., Krasotkina, O., Mottl, V. and Windridge, D. 2014. Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection. in: Aleskerov, F., Goldengorin, B. and Pardalos, P. (ed.) Clusters, Orders, and Trees: Methods and Applications: In Honor of Boris Mirkin's 70th Birthday New York Springer.Trained eyes: experience promotes adaptive gaze control in dynamic and uncertain visual environments
Taya, S., Windridge, D. and Osman, M. 2013. Trained eyes: experience promotes adaptive gaze control in dynamic and uncertain visual environments. PLoS ONE. 8 (8). https://doi.org/10.1371/journal.pone.0071371High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)
Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557Automatic annotation of court games with structured output learning
Yan, F., Kittler, J., Mikolajczyk, K. and Windridge, D. 2012. Automatic annotation of court games with structured output learning. 21st International Conference on Pattern Recognition. Tsukuba, Japan 11 - 15 Nov 2012 IEEE. pp. 3577-3580 https://doi.org/https://ieeexplore.ieee.org/document/6460938Characterizing driver intention via hierarchical perception-action modeling
Windridge, D., Shaukat, A. and Hollnagel, E. 2013. Characterizing driver intention via hierarchical perception-action modeling. IEEE Transactions on Human-Machine Systems. 43 (1), pp. 17-31. https://doi.org/10.1109/TSMCA.2012.2216868On-line spatio-temporal independent component analysis for motion correction in renal DCE-MRI
Kiani, S., Gordon, I., Windridge, D. and Wells, K. 2012. On-line spatio-temporal independent component analysis for motion correction in renal DCE-MRI. 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC). Anaheim, CA, USA 27 Oct - 03 Nov 2012 IEEE. https://doi.org/10.1109/nssmic.2012.6551664Application of the multi-modal relevance vector machine to the problem of protein secondary structure prediction
Razin, N., Sungurov, D., Mottl, V., Torshin, I., Sulimova, V., Seredin, O. and Windridge, D. 2012. Application of the multi-modal relevance vector machine to the problem of protein secondary structure prediction. 7th IAPR International Conference on Pattern Recognition in Bioinformatics. Tokyo, Japan 08 - 10 Nov 2012 Springer. pp. 153-165 https://doi.org/10.1007/978-3-642-34123-6_14A framework for hierarchical perception-action learning utilizing fuzzy reasoning
Windridge, D., Felsberg, M. and Shaukat, A. 2013. A framework for hierarchical perception-action learning utilizing fuzzy reasoning. IEEE Transactions on Cybernetics. 43 (1), pp. 155-169. https://doi.org/10.1109/tsmcb.2012.2202109Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060Anomaly detection and knowledge transfer in automatic sports video annotation
Almajai, I., Yan, F., De Campos, T., Khan, A., Christmas, W., Windridge, D. and Kittler, J. 2011. Anomaly detection and knowledge transfer in automatic sports video annotation. in: Weinshall, D., Anemüller, J. and Gool, L. (ed.) Detection and Identification of Rare Audio-visual Cues Springer. pp. 109-117A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets
Panov, M., Tatarchuk, A., Mottl, V. and Windridge, D. 2011. A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets. 10th International Workshop on Multiple Classifier Systems. Naples, Italy 15 - 17 Jun 2011 Springer. https://doi.org/10.1007/978-3-642-21557-5_15An evaluation of bags-of-words and spatio-temporal shapes for action recognition
de Campos, T., Barnard, M., Mikolajczyk, K., Kittler, J., Yan, F., Christmas, W. and Windridge, D. 2011. An evaluation of bags-of-words and spatio-temporal shapes for action recognition. 2011 IEEE Workshop on Applications of Computer Vision (WACV). Kona, HI, USA 05 - 07 Jan 2011 IEEE. https://doi.org/10.1109/WACV.2011.5711524Ball event recognition using HMM for automatic tennis annotation
Almajai, I., Kittler, J., De Campos, T., Christmas, W., Yan, F., Windridge, D. and Khan, A. 2010. Ball event recognition using HMM for automatic tennis annotation. 2010 IEEE International Conference on Image Processing. Hong Kong, China 26 - 29 Sep 2010 IEEE. https://doi.org/10.1109/ICIP.2010.5652415Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535A support kernel machine for supervised selective combining of diverse pattern-recognition modalities
Tatarchuk, A., Urlov, E., Mottl, V. and Windridge, D. 2010. A support kernel machine for supervised selective combining of diverse pattern-recognition modalities. 9th International Workshop on Multiple Classifier Systems. Cairo, Egypt 07 - 09 Apr 2010 Springer. https://doi.org/10.1007/978-3-642-12127-2_17Adaptive, perception-action-based cognitive modelling of human driving behaviour using control, gaze and signal inputs
Shaukat, A., Windridge, D., Hollnagel, E., Macchi, L. and Kittler, J. 2010. Adaptive, perception-action-based cognitive modelling of human driving behaviour using control, gaze and signal inputs. Brain Inspired Cognitive Systems - BICS 2010. Madrid, Spain 14 - 16 Jul 2010Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation
Windridge, D. 2009. Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation. Hussain, A., Aleksander, I., Smith, L., Barros, A., Chrisley, R. and Cutsuridis, V. (ed.) Brain Inspired Cognitive Systems 2008. São Luís, Brazil 24 - 27 Jul 2008 Springer. pp. 95-134 https://doi.org/10.1007/978-0-387-79100-5_6A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception-action architectures
Shevchenko, M., Windridge, D. and Kittler, J. 2009. A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception-action architectures. Image and Vision Computing. 27 (11), pp. 1702-1714. https://doi.org/10.1016/j.imavis.2008.12.002An entropy-based approach to the hierarchical acquisition of perception-action capabilities
Windridge, D., Shevchenko, M. and Kittler, J. 2008. An entropy-based approach to the hierarchical acquisition of perception-action capabilities. 4th International Workshop on Cognitive Vision. Santorini, Greece 12 May 2008 Springer. https://doi.org/10.1007/978-3-540-92781-5_7Cognitive learning with automatic goal acquisition
Kittler, J., Shevchenko, M. and Windridge, D. 2006. Cognitive learning with automatic goal acquisition. 3rd European Starting AI Researcher Symposium. Riva del Garda, Italy 28 - 29 Aug 2006 IOS Press. https://doi.org/10.5555/1565192.1565195A memory architecture and contextual reasoning framework for cognitive vision
Kittler, J., Christmas, W.J., Kostin, A., Yan, F., Kolonias, I. and Windridge, D. 2005. A memory architecture and contextual reasoning framework for cognitive vision. 14th Scandinavian Conference on Image Analysis. Joensuu, Finland 19 - 22 Jun 2005 Springer. https://doi.org/10.1007/11499145_36A linguistic feature vector for the visual interpretation of sign language
Bowden, R., Windridge, D., Kadir, T., Zisserman, A. and Brady, M. 2004. A linguistic feature vector for the visual interpretation of sign language. 8th European Conference on Computer Vision. Prague, Czech Republic 11 - 14 May 2004 Springer. https://doi.org/10.1007/978-3-540-24670-1_30A general strategy for hidden markov chain parameterisation in composite feature-spaces
Windridge, D., Bowden, R. and Kittler, J. 2004. A general strategy for hidden markov chain parameterisation in composite feature-spaces. Joint IAPR International Workshops, SSPR 2004 and SPR 2004. Lisbon, Portugal 18 - 20 Aug 2004 Springer. pp. 1069-1077 https://doi.org/10.1007/978-3-540-27868-9_118A morphologically optimal strategy for classifier combination: Multiple expert fusion as a tomographic process
Windridge, D. and Kittler, J. 2003. A morphologically optimal strategy for classifier combination: Multiple expert fusion as a tomographic process. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25 (3), pp. 343-353. https://doi.org/10.1109/TPAMI.2003.1182097Classifier combination as a tomographic process
Windridge, D. and Kittler, J. 2001. Classifier combination as a tomographic process. 2nd International Workshop on Multiple Classifier Systems. Cambridge, UK 02 - 04 Jul 2001 Springer. pp. 248-258 https://doi.org/10.1007/3-540-48219-9_25Boosting multiple experts by joint optimization of decision thresholds
Kittler, J., Yusoff, Y., Christmas, W., Windeatt, T. and Windridge, D. 2001. Boosting multiple experts by joint optimization of decision thresholds. Pattern Recognition and Image Analysis. 11 (3), pp. 529-541.A fluctuation analysis for optical cluster galaxies - I. Theory
Windridge, D. and Phillipps, S. 2000. A fluctuation analysis for optical cluster galaxies - I. Theory. Monthly Notices of the Royal Astronomical Society (MNRAS). 319 (2), pp. 591-605. https://doi.org/10.1046/j.1365-8711.2000.03908.xAn optimal solution to the problem of multiple expert fusion
Windridge, D. 2000. An optimal solution to the problem of multiple expert fusion. University of Surrey.24921
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