Staff profile

Professor Paolo Remagnino has worked in computer vision and artificial intelligence for over 30 years. Professor Remagnino research is on the development of innovative methods for image and video interpretation, making wide use of pattern recognition, machine and deep learning and distributed intelligence techniques. Professor Remagnino has published over 180 scientific articles in international conferences and high impact journals. Prof. Remagnino has secured research grants funded by most scientific funding bodies, including the NATEP, Innovate UK, EPSRC, MRC, Leverhulme Trust, EU (FP7 and H2020) and the US DHS. At present, Prof. Remagnino is the principal investigator of a project on the development of machine learning algorithms for the automatic assessment of the health of natural habitats (https://www.nih2020.eu/).

• Artificial intelligence
• Image and Video Analysis
• Machine Learning
• Pattern Recognition
• Physical Security

• 2000:

  EPRSC college 

  : EPRSC college 
• 2000:

  JSPS fellow 

  : JSPS fellow 
• 2000:

  UK Research and Innovation Fellowships Peer Review College member

  : UK Research and Innovation Fellowships Peer Review College member
• 2000:

  Visiting Researcher at the Royal Botanic Gardens, Kew 

  : Visiting Researcher at the Royal Botanic Gardens, Kew 

Conference Paper

• Kaur, P., Gigante, D., Caccianiga, M., Bagella, S., Angiolini, C., Garabini, M., …Remagnino, P. (in press). Segmentation and Identification of Mediterranean Plant Species.
• Lim, J. H., Tan, C. S., Chan, C. S., Welikala, R. A., Remagnino, P., Rajendran, S., …Barman, S. A. (2021). D'OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer. In B. Papiez, M. Yaqub, J. Jiao, A. Namburete, & J. Noble (Eds.), . https://doi.org/10.1007/978-3-030-80432-9%5C_31
• Saeed, R., Recupero, D. R., & Remagnino, P. (2021). Simulating People Dynamics. . https://doi.org/10.1109/ie51775.2021.9486478
• Khadka, A., Argyriou, V., & Remagnino, P. (2020). Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. . https://doi.org/10.1109/dcoss49796.2020.00049
• Khadka, A., Remagnino, P., & Argyriou, V. (2020). SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS.
• Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Urban Scene Segmentation using Semi-supervised GAN. In L. Bruzzone, F. Bovolo, & J. Benediktsson (Eds.), . https://doi.org/10.1117/12.2533055
• Oghaz, M. M., Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019). Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. . https://doi.org/10.1109/dcoss.2019.00078
• Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2019). Latent Bernoulli Autoencoder.
• Rimboux, A., Dupre, R., Daci, E., Lagkas, T., Sarigiannidis, P., Remagnino, P., & Argyriou, V. (2019). Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation. . https://doi.org/10.1109/dcoss.2019.00070
• Kim, C. E., Oghaz, M. M. D., Fajtl, J., Argyriou, V., & Remagnino, P. (2019). A Comparison of Embedded Deep Learning Methods for Person Detection. In A. Tremeau, G. Farinella, & J. Braz (Eds.), . https://doi.org/10.5220/0007386304590465
• Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Smart Monitoring of Crops Using Generative Adversarial Networks. In M. Vento, & G. Percannella (Eds.), . https://doi.org/10.1007/978-3-030-29888-3%5C_45
• Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019). Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images. In D. Tzovaras, D. Giakoumis, M. Vincze, & A. Argyros (Eds.), . https://doi.org/10.1007/978-3-030-34995-0%5C_71
• Razaak, M., Kerdegari, H., Davies, E., Abozariba, R., Broadbent, M., Mason, K., …Remagnino, P. (2019). An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies. In M. Vento, & G. Percannella (Eds.), . https://doi.org/10.1007/978-3-030-29930-9%5C_11
• Khadka, A. R., Remagnino, P., & Argyriou, V. (2018). Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering. In G. DiBaja, L. Gallo, K. Yetongnon, A. Dipanda, M. CastrillonSantana, & R. Chbeir (Eds.), . https://doi.org/10.1109/sitis.2018.00039
• Lee, S. H., Chang, Y. L., Chan, C. S., & Remagnino, P. (2017). HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION.
• Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015). DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS.
• Fraz, M., Remagnino, P., Hoppe, A., & Barman, S. (2013). Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier.
• Fraz, M. M., Remagnino, P., Hoppe, A., Barman, S. A., Rudnicka, A., Owen, C., & Whincup, P. (2012). A model based approach for vessel caliber measurement in retinal images. In K. Yetongnon, R. Chbeir, A. Dipanda, & L. Gallo (Eds.), . https://doi.org/10.1109/sitis.2012.29
• Cope, J. S., & Remagnino, P. (2012). Classifying Plant Leaves from Their Margins Using Dynamic Time Warping. In J. BlancTalon, W. Philips, D. Popescu, P. Scheunders, & P. Zemcik (Eds.),
• Fraz, M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A., Owen, C., & Barman, S. (2012). Ensemble Classification System Applied for Retinal Vessel Segmentation on Child Images Containing Various Vessel Profiles. In A. Campilho, & M. Kamel (Eds.),
• Cope, J. S., & Remagnino, P. (2012). Classification of High-Dimension PDFs Using the Hungarian Algorithm. In G. Gimelfarb, E. Hancock, A. Imiya, A. Kuijper, M. Kudo, S. Omachi, …K. Yamada (Eds.),

Journal Article

• Olvera-Barrios, A., Rudnicka, A. R., Anderson, J., Bolter, L., Chambers, R., Warwick, A. N., …Owen, C. G. (2023). Two-year recall for people with no diabetic retinopathy: a multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect. British Journal of Ophthalmology, 1839-1845. https://doi.org/10.1136/bjo-2023-324097
• Olvera-Barrios, A., Owen, C. G., Anderson, J., Warwick, A. N., Chambers, R., Bolter, L., …Rudnicka, A. R. (2023). Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study. BMJ Open Diabetes Research and Care, 11(6), Article e003683. https://doi.org/10.1136/bmjdrc-2023-003683
• Angelini, F., Angelini, P., Angiolini, C., Bagella, S., Bonomo, F., Caccianiga, M., …Garabini, M. (2023). Robotic Monitoring of Habitats: The Natural Intelligence Approach. IEEE Access, 11, 72575-72591. https://doi.org/10.1109/access.2023.3294276
• Maktab Dar Oghaz, M., Razaak, M., & Remagnino, P. (2022). Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution. Sensors, 22(12), Article 4339. https://doi.org/10.3390/s22124339
• Saeed, R., Recupero, D. R., & Remagnino, P. (2022). Simulating crowd behaviour combining both microscopic and macroscopic rules. Information Sciences, 583, 137-158. https://doi.org/10.1016/j.ins.2021.11.028
• Saeed, R. A., Recupero, D. R., & Remagnino, P. (2021). The boundary node method for multi-robot multi-goal path planning problems. Expert Systems, 38(6), Article e12691. https://doi.org/10.1111/exsy.12691
• Chang, Y., Chan, C., & Remagnino, P. (2021). Action recognition on continuous video. https://doi.org/10.1007/s00521-020-04982-9
• Saeed, R., Recupero, D. R., & Remagnino, P. (2020). A Boundary Node Method for path planning of mobile robots. Robotics and Autonomous Systems, 123, Article 103320. https://doi.org/10.1016/j.robot.2019.103320
• Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., …Barman, S. A. (2020). Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer. IEEE Access, 8, 132677-132693. https://doi.org/10.1109/access.2020.3010180
• Dupre, R., Fajtl, J., Argyriou, V., & Remagnino, P. (2020). Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning. IEEE Transactions on Image Processing, 29, 4337-4348. https://doi.org/10.1109/tip.2019.2913986
• Lee, S. H., Chan, C. S., & Remagnino, P. (2018). Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks. IEEE Transactions on Image Processing, 27(9), 4287-4301. https://doi.org/10.1109/tip.2018.2836321
• Lee, S. H., Chan, C. S., Mayo, S. J., & Remagnino, P. (2017). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71, 1-13. https://doi.org/10.1016/j.patcog.2017.05.015
• Thida, M., Eng, H., & Remagnino, P. (2013). Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes. IEEE Transactions on Cybernetics, 43(6), 2147-2156. https://doi.org/10.1109/tcyb.2013.2242059
• Cope, J., Remagnino, P., Mannan, S., Diaz, K., Ferri, F., & Wilkin, P. (2013). Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm. Expert Systems with Applications, 40(17), 6707-6712. https://doi.org/10.1016/j.eswa.2013.05.042
• Fraz, M., Remagnino, P., Hoppe, A., Rudnicka, A., Owen, C., Whincup, P., & Barman, S. (2013). Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach. Computerized Medical Imaging and Graphics, 37(1), 48-60. https://doi.org/10.1016/j.compmedimag.2013.01.004
• Fraz, M., Barman, S., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., …Owen, C. (2012). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer Methods and Programs in Biomedicine, 108(2), 600-616. https://doi.org/10.1016/j.cmpb.2011.08.009
• Fraz, M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A., Owen, C., & Barman, S. (2012). Blood vessel segmentation methodologies in retinal images - A survey. Computer Methods and Programs in Biomedicine, 108(1), 407-433. https://doi.org/10.1016/j.cmpb.2012.03.009
• Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012). An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation. IEEE Transactions on Biomedical Engineering, 59(9), 2538-2548. https://doi.org/10.1109/tbme.2012.2205687