Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach
(2013)
Journal Article
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
Outputs (663)
Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks (2018)
Journal Article
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
Action recognition on continuous video (2021)
Journal Article
Chang, Y., Chan, C., & Remagnino, P. (2021). Action recognition on continuous video. https://doi.org/10.1007/s00521-020-04982-9
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012)
Journal Article
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
How deep learning extracts and learns leaf features for plant classification (2017)
Journal Article
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
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
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
CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy (2022)
Journal Article
Zhang, H., Ho, E. S., & Shum, H. P. (2022). CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy. Software impacts, 14, Article 100419. https://doi.org/10.1016/j.simpa.2022.100419Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA).... Read More about CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy.
A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction (2022)
Journal Article
Zhu, M., Men, Q., Ho, E. S., Leung, H., & Shum, H. P. (2022). A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. Journal of Medical Systems, 46(11), Article 76. https://doi.org/10.1007/s10916-022-01857-5Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations... Read More about A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.
A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification (2022)
Journal Article
Winterbottom, T., Leone, A., & Al Moubayed, N. (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports, 12(1), Article 13468. https://doi.org/10.1038/s41598-022-15965-2We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem o... Read More about A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification.
Blood vessel segmentation methodologies in retinal images - A survey (2012)
Journal Article
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
Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm (2013)
Journal Article
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
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
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
Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field (2022)
Journal Article
Xie, S., Hu, J., Bhowmick, P., Ding, Z., & Arvin, F. (2022). Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field. IEEE Transactions on Intelligent Transportation Systems, 23(11), 21531- 21547. https://doi.org/10.1109/tits.2022.3189741Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper propo... Read More about Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field.
Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation (2022)
Journal Article
Crosato, L., Shum, H. P., Ho, E. S., & Wei, C. (2023). Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation. IEEE Transactions on Intelligent Vehicles, 8(2), 1339-1349. https://doi.org/10.1109/tiv.2022.3189836Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutua... Read More about Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation.
Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels (2022)
Journal Article
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science, 8(e974), Article e974. https://doi.org/10.7717/peerj-cs.974Bilinear pooling (BLP) refers to a family of operations recently developed for fusing features from different modalities predominantly for visual question answering (VQA) models. Successive BLP techniques have yielded higher performance with lower co... Read More about Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels.
Colias: An Autonomous Micro Robot for Swarm Robotic Applications (2014)
Journal Article
Arvin, F., Murray, J., Zhang, C., & Yue, S. (2014). Colias: An Autonomous Micro Robot for Swarm Robotic Applications. International Journal of Advanced Robotic Systems, 11(7), https://doi.org/10.5772/58730Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of... Read More about Colias: An Autonomous Micro Robot for Swarm Robotic Applications.
Mona: an Affordable Open-Source Mobile Robot for Education and Research (2018)
Journal Article
Arvin, F., Espinosa, J., Bird, B., West, A., Watson, S., & Lennox, B. (2019). Mona: an Affordable Open-Source Mobile Robot for Education and Research. Journal of Intelligent and Robotic Systems, 94(3-4), 761–775. https://doi.org/10.1007/s10846-018-0866-9Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread... Read More about Mona: an Affordable Open-Source Mobile Robot for Education and Research.
Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots (2020)
Journal Article
Liu, Z., West, C., Lennox, B., & Arvin, F. (2020). Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots. Sensors, 20(11), Article 3308. https://doi.org/10.3390/s20113308Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and ang... Read More about Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots.