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

• Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration
  
  Liu, R., Remagnino, P., & Shum, H. P. (2025). Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration. In Proceedings of the 2024 International Conference on Pattern Recognition (pp. 181-195). Springer. https://doi.org/10.1007/978-3-031-78122-3_12
• Segmentation and Identification of Mediterranean Plant Species
  
  Kaur, P., Gigante, D., Caccianiga, M., Bagella, S., Angiolini, C., Garabini, M., Angelini, F., & Remagnino, P. (2023). Segmentation and Identification of Mediterranean Plant Species. In Advances in Visual Computing 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part II (pp. 431-442). Springer. https://doi.org/10.1007/978-3-031-47966-3_34
• D'OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer
  
  Lim, J. H., Tan, C. S., Chan, C. S., Welikala, R. A., Remagnino, P., Rajendran, S., Kallarakkal, T. G., Zain, R. B., Jayasinghe, R. D., Rimal, J., Kerr, A. R., Amtha, R., Patil, K., Tilakaratne, W. M., Gibson, J., Cheong, S. C., & 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.), Medical Image Understanding and Analysis 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings (pp. 408-422). Springer. https://doi.org/10.1007/978-3-030-80432-9_31
• Simulating People Dynamics
  
  Saeed, R., Recupero, D. R., & Remagnino, P. (2021). Simulating People Dynamics. In International Conference on Intelligent Environments. https://doi.org/10.1109/ie51775.2021.9486478
• SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS
  
  Khadka, A., Remagnino, P., & Argyriou, V. (2020). SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS. In International Conference on Acoustics Speech and Signal Processing ICASSP (pp. 4052-4056).
• Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices
  
  Khadka, A., Argyriou, V., & Remagnino, P. (2020). Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. In IEEE International Conference on Distributed Computing in Sensor Systems (pp. 260-264). https://doi.org/10.1109/dcoss49796.2020.00049
• Urban Scene Segmentation using Semi-supervised GAN
  
  Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Urban Scene Segmentation using Semi-supervised GAN. In L. Bruzzone, F. Bovolo, & J. Benediktsson (Eds.), Proceedings of SPIE. https://doi.org/10.1117/12.2533055
• A Comparison of Embedded Deep Learning Methods for Person Detection
  
  Kim, C. E., Oghaz, M. M. D., Fajtl, J., Argyriou, V., & Remagnino, P. (2019). A Comparison of Embedded Deep Learning Methods for Person Detection (A. Tremeau, G. Farinella, & J. Braz, Eds.). https://doi.org/10.5220/0007386304590465
• Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
  
  Oghaz, M. M., Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019). Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. In IEEE International Conference on Distributed Computing in Sensor Systems (pp. 362-369). https://doi.org/10.1109/dcoss.2019.00078
• Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation
  
  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. In IEEE International Conference on Distributed Computing in Sensor Systems (pp. 304-311). https://doi.org/10.1109/dcoss.2019.00070
• Latent Bernoulli Autoencoder
  
  Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2019). Latent Bernoulli Autoencoder. Presented at 25TH AMERICAS CONFERENCE ON INFORMATION SYSTEMS (AMCIS 2019) Assoc Informat Syst.
• An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies
  
  Razaak, M., Kerdegari, H., Davies, E., Abozariba, R., Broadbent, M., Mason, K., Argyriou, V., & Remagnino, P. (2019). An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies. In M. Vento & G. Percannella (Eds.), Communications in Computer and Information Science (pp. 109-119). https://doi.org/10.1007/978-3-030-29930-9%5C_11
• Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images
  
  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.), Lecture Notes in Computer Science (pp. 778-786). https://doi.org/10.1007/978-3-030-34995-0%5C_71
• Smart Monitoring of Crops Using Generative Adversarial Networks
  
  Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Smart Monitoring of Crops Using Generative Adversarial Networks. In M. Vento & G. Percannella (Eds.), Lecture Notes in Computer Science (pp. 554-563). https://doi.org/10.1007/978-3-030-29888-3%5C_45
• Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering
  
  Khadka, A. R., Remagnino, P., & Argyriou, V. (2018). Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering (G. DiBaja, L. Gallo, K. Yetongnon, A. Dipanda, M. CastrillonSantana, & R. Chbeir, Eds.). https://doi.org/10.1109/sitis.2018.00039
• HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION
  
  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. In IEEE International Conference on Image Processing ICIP (pp. 4462-4466).
• DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS
  
  Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015). DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS. In IEEE International Conference on Image Processing ICIP (pp. 452-456).
• Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier
  
  Fraz, M., Remagnino, P., Hoppe, A., & Barman, S. (2013). Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier. Presented at INTERNATIONAL CONFERENCE ON COMPUTER MEDICAL APPLICATIONS (ICCMA’ 2013) IEEE, Tunisia Sect; Dar Al Uloom Univ; N\&N Global Technologies; Future Technologies \& Innovat.
• Ensemble Classification System Applied for Retinal Vessel Segmentation on Child Images Containing Various Vessel Profiles
  
  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.), Lecture Notes in Computer Science (pp. 380-389).
• Classification of High-Dimension PDFs Using the Hungarian Algorithm
  
  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, T. Windeatt, & K. Yamada (Eds.), Lecture Notes in Computer Science (pp. 727-733).
• Classifying Plant Leaves from Their Margins Using Dynamic Time Warping
  
  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.), Lecture Notes in Computer Science (pp. 258-267).
• A model based approach for vessel caliber measurement in retinal images
  
  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 (K. Yetongnon, R. Chbeir, A. Dipanda, & L. Gallo, Eds.). https://doi.org/10.1109/sitis.2012.29

Journal Article

• 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
  
  Olvera-Barrios, A., Rudnicka, A. R., Anderson, J., Bolter, L., Chambers, R., Warwick, A. N., Welikala, R., Fajtl, J., Barman, S., Remagnino, P., Wu, Y., Lee, A., Chew, E. Y., Ferris, F. L., Hingorani, A. D., Sofat, R., Egan, C., Tufail, A., & 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, 107(12), 1839-1845. https://doi.org/10.1136/bjo-2023-324097
• Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study
  
  Olvera-Barrios, A., Owen, C. G., Anderson, J., Warwick, A. N., Chambers, R., Bolter, L., Wu, Y., Welikala, R., Fajtl, J., Barman, S., Remagnino, P., Chew, E. Y., Ferris, F. L., Hingorani, A. D., Sofat, R., Lee, A., Egan, C., Tufail, A., & 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
• Robotic Monitoring of Habitats: The Natural Intelligence Approach
  
  Angelini, F., Angelini, P., Angiolini, C., Bagella, S., Bonomo, F., Caccianiga, M., Santina, C. D., Gigante, D., Hutter, M., Nanayakkara, T., Remagnino, P., Torricelli, D., & Garabini, M. (2023). Robotic Monitoring of Habitats: The Natural Intelligence Approach. IEEE Access, 11, 72575-72591. https://doi.org/10.1109/access.2023.3294276
• Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution
  
  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
• Simulating crowd behaviour combining both microscopic and macroscopic rules
  
  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
• The boundary node method for multi-robot multi-goal path planning problems
  
  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
• Action recognition on continuous video
  
  Chang, Y., Chan, C., & Remagnino, P. (2021). Action recognition on continuous video. Neural Computing and Applications, 33(4), 1233-1243. https://doi.org/10.1007/s00521-020-04982-9
• A Boundary Node Method for path planning of mobile robots
  
  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
• Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning
  
  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
• Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer
  
  Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., Zain, R. B., Jayasinghe, R. D., Rimal, J., Kerr, A. R., Amtha, R., Patil, K., Tilakaratne, W. M., Gibson, J., Cheong, S. C., & 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
• Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks
  
  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
• How deep learning extracts and learns leaf features for plant classification
  
  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
• Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes
  
  Thida, M., Eng, H.-L., & 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
• Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm
  
  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
• Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach
  
  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
• An approach to localize the retinal blood vessels using bit planes and centerline detection
  
  Fraz, M., Barman, S., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A., & 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
• Blood vessel segmentation methodologies in retinal images - A survey
  
  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
• An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
  
  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