Professor Paolo Remagnino's Outputs (40)

Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration (2024)
Presentation / Conference Contribution
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

We introduce neural-code PIFu, a novel implicit function for 3D human reconstruction, leveraging neural codebooks, our approach learns recurrent patterns in the feature space and reuses them to improve current features. Many existing methods predict... Read More about Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration.

Segmentation and Identification of Mediterranean Plant Species (2023)
Presentation / Conference Contribution
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

Recently, object recognition and image segmentation have gained much attention in the computer vision field and image processing for effective object localisation and identification. Researchers have applied semantic segmentation and instance segment... Read More about Segmentation and Identification of Mediterranean Plant Species.

Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study (2023)
Journal Article
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

IntroductionThe English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual eye screening. We examined incidence and determinants of sight-threatening diabetic retinopathy (STDR) in a sociodemographically diverse m... Read More about Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study.

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 (2023)
Journal Article
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

Background/aims: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group... Read More about 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.

Robotic Monitoring of Habitats: The Natural Intelligence Approach (2023)
Journal Article
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

In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is... Read More about Robotic Monitoring of Habitats: The Natural Intelligence Approach.

Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution (2022)
Journal Article
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 (2022)
Journal Article
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

Simulating People Dynamics (2021)
Presentation / Conference Contribution
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

The boundary node method for multi-robot multi-goal path planning problems (2021)
Journal Article
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

D'OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer (2021)
Presentation / Conference Contribution
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

Action recognition on continuous video (2021)
Journal Article
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

Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices (2020)
Presentation / Conference Contribution
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

SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS (2020)
Presentation / Conference Contribution
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

Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning (2020)
Journal Article
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 (2020)
Journal Article
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

A Boundary Node Method for path planning of mobile robots (2020)
Journal Article
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

An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies (2019)
Presentation / Conference Contribution
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

Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation (2019)
Presentation / Conference Contribution
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

A Comparison of Embedded Deep Learning Methods for Person Detection (2019)
Presentation / Conference Contribution
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

Smart Monitoring of Crops Using Generative Adversarial Networks (2019)
Presentation / Conference Contribution
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