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. (2024, December). Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration. Presented at 2024 International Conference on Pattern Recognition, Kolkata, India
• Segmentation and Identification of Mediterranean Plant Species
  Kaur, P., Gigante, D., Caccianiga, M., Bagella, S., Angiolini, C., Garabini, M., Angelini, F., & Remagnino, P. (2023, October). Segmentation and Identification of Mediterranean Plant Species. Presented at ISVC 2023: 18th International Symposium on Visual Computing, Lake Tahoe, NV
• 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, July). D'OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer. Presented at MEDICAL IMAGE UNDERSTANDING AND ANALYSIS (MIUA 2021) Univ Oxford; Univ Oxford, Med Sci Div, Nuffield Dept Clin Neurosciences; Big Data Inst; Math Phys \& Life Sci Div; Inst Biomed Engn; MathWorks; Brainomix; Journal Imaging; Oxford Univ Innovat; nVidia; I
• Simulating People Dynamics
  Saeed, R., Recupero, D. R., & Remagnino, P. (2021, December). Simulating People Dynamics. Presented at 2021 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE) IEEE; Middlesex Univ London; Sapienza Univ Roma; Univ Grenoble Alpes; Queensland Univ Technol; IEEE Syst Man \& Cybernet Soc; IOS Press; Assoc Advancement Artificial Intelligence; Technol
• SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS
  Khadka, A., Remagnino, P., & Argyriou, V. (2020, December). SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS. Presented at 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING Inst Elect \& Elect Engineers; Inst Elect \& Elect Engineers, Signal Proc Soc
• Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices
  Khadka, A., Argyriou, V., & Remagnino, P. (2020, December). Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. Presented at 16TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2020) IEEE Comp Soc
• A Comparison of Embedded Deep Learning Methods for Person Detection
  Kim, C. E., Oghaz, M. M. D., Fajtl, J., Argyriou, V., & Remagnino, P. (2019, December). A Comparison of Embedded Deep Learning Methods for Person Detection. Presented at PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5
• Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
  Oghaz, M. M., Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019, December). Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. Presented at 2019 15TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS) IEEE Comp Soc
• Urban Scene Segmentation using Semi-supervised GAN
  Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019, December). Urban Scene Segmentation using Semi-supervised GAN. Presented at IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXV SPIE
• 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, December). Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation. Presented at 2019 15TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS) IEEE Comp Soc
• Latent Bernoulli Autoencoder
  Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2019, December). 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, December). An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies. Presented at COMPUTER ANALYSIS OF IMAGES AND PATTERNS (CAIP 2019) Univ Salerno, Dept Comp \& Elect Engn \& Appl Math, Intelligent Machines Recognit Video Images \& Audio Lab; A I Tech srl; SAST Gmbh; AI4Health srl; Gesan srl; Hanwha Techwin Europe Ltd; Nexsoft SpA
• Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images
  Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019, December). Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images. Presented at COMPUTER VISION SYSTEMS (ICVS 2019)
• Smart Monitoring of Crops Using Generative Adversarial Networks
  Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019, December). Smart Monitoring of Crops Using Generative Adversarial Networks. Presented at COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT I Univ Salerno, Dept Comp \& Elect Engn \& Appl Math; SAST Gmbh; A I Tech srl; AI4Health srl; Nexsoft spa; Gesan srl; Hanwha Techwin Europe Ltd; Springer Lecture Notes Comp Sci; Italian Assoc Comp Vi
• Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering
  Khadka, A. R., Remagnino, P., & Argyriou, V. (2018, December). Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering. Presented at 2018 14TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY \& INTERNET BASED SYSTEMS (SITIS) IEEE Comp Soc; Univ Las Palmas Gran Canaria; Univ Milan; Univ Bourgogne, Laboratoire Electronique Image Informatique Res Grp; Natl Res Council Italy, Inst High
• 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, December). HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION. Presented at 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) Inst Elect \& Elect Engineers; Inst Elect \& Elect Engineers Signal Proc Soc
• DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS
  Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015, December). DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS. Presented at 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) Inst Elect \& Elect Engineers; IEEE Signal Proc Soc
• Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier
  Fraz, M., Remagnino, P., Hoppe, A., & Barman, S. (2013, December). 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, December). Ensemble Classification System Applied for Retinal Vessel Segmentation on Child Images Containing Various Vessel Profiles. Presented at IMAGE ANALYSIS AND RECOGNITION, PT II Assoc Image \& Machine Intelligence (AIMI)
• Classification of High-Dimension PDFs Using the Hungarian Algorithm
  Cope, J. S., & Remagnino, P. (2012, December). Classification of High-Dimension PDFs Using the Hungarian Algorithm. Presented at STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION Int Assoc Pattern Recognit; Int Assoc Pattern Recognit, Tech Comm 1, Stat Pattern Recognit Tech; Int Assoc Pattern Recognit, Tech Comm 2, Struct \& Syntact Pattern Recognit; Tohoku Univ; Hiroshima
• Classifying Plant Leaves from Their Margins Using Dynamic Time Warping
  Cope, J. S., & Remagnino, P. (2012, December). Classifying Plant Leaves from Their Margins Using Dynamic Time Warping. Presented at ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS (ACIVS 2012) Brno Univ Technol; Camea; Ghent Univ; Honeywell; Redhat; Unis; Zoner
• 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, December). A model based approach for vessel caliber measurement in retinal images. Presented at 8TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY \& INTERNET BASED SYSTEMS (SITIS 2012) IEEE Comp Soc; INCAR

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