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Outputs (2673)

Towards Graph Representation Learning Based Surgical Workflow Anticipation (2022)
Presentation / Conference Contribution
Zhang, X., Al Moubayed, N., & Shum, H. P. (2022). Towards Graph Representation Learning Based Surgical Workflow Anticipation. . https://doi.org/10.1109/bhi56158.2022.9926801

Surgical workflow anticipation can give predictions on what steps to conduct or what instruments to use next, which is an essential part of the computer-assisted intervention system for surgery, e.g. workflow reasoning in robotic surgery. However, cu... Read More about Towards Graph Representation Learning Based Surgical Workflow Anticipation.

La madurez gay masculina en el cine de Ventura Pons (2022)
Presentation / Conference Contribution
Fouz-Hernández, S. (2022, October). La madurez gay masculina en el cine de Ventura Pons. Paper presented at X Congreso Internacional de GECA: Cine Queer en el Mundo, Universidad Complutense Madrid

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos (2022)
Presentation / Conference Contribution
Qiao, T., Men, Q., Li, F. W., Kubotani, Y., Morishima, S., & Shum, H. P. (2022). Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos. . https://doi.org/10.1007/978-3-031-19772-7_28

Human-Object Interaction (HOI) recognition in videos is important for analysing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when... Read More about Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos.

Adaptive optical methods for in vivo imaging in developing Zebra fish (2012)
Presentation / Conference Contribution
Girkin, J., Taylor, J., Bourgenot, C., Saunter, C., & Love, G. (2012). Adaptive optical methods for in vivo imaging in developing Zebra fish. In 2012 International Symposium on Optomechatronic Technologies (ISOT 2012). https://doi.org/10.1109/isot.2012.6403295

The humble Zebra fish is rapidly establishing itself as the model of choice for a wide range of biological investigations, in particular at the developing embryo stage. Single Plane Illumination Microscopy (SPIM) has already been shown to be a very p... Read More about Adaptive optical methods for in vivo imaging in developing Zebra fish.

Aging Gay Men in the Films of Ventura Pons (2023)
Presentation / Conference Contribution
Fouz-Hernández, S. (2023, January). Aging Gay Men in the Films of Ventura Pons. Paper presented at MLA Annual Convention, San Francisco, USA

Heart synchronization for SPIM microscopy of living zebra fish (2011)
Presentation / Conference Contribution
Taylor, J., Saunter, C., Chaudhry, B., Henderson, D., Love, G., & Girkin, J. (2011). Heart synchronization for SPIM microscopy of living zebra fish. In J. Conchello, C. J. Cogswell, T. Wilson, & T. G. Brown (Eds.), . https://doi.org/10.1117/12.874773

We describe work on producing a selective plane illumination microscope for cardiac imaging in zebra fish embryos. The system has a novel synchronization system for imaging oscillating structures (e.g. the heart) and will have adaptive optics for ima... Read More about Heart synchronization for SPIM microscopy of living zebra fish.

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications (2021)
Presentation / Conference Contribution
Li, L., Ismail, K. N., Shum, H. P., & Breckon, T. P. (2021). DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. . https://doi.org/10.1109/3dv53792.2021.00130

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq... Read More about DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications.

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery (2022)
Presentation / Conference Contribution
Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. (2022). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. . https://doi.org/10.5220/0010836600003124

As unmanned aerial vehicles (UAV) become more accessible with a growing range of applications, the risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single ca... Read More about UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Presentation / Conference Contribution
Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. . https://doi.org/10.1109/iccvw54120.2021.00142

Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems. Common technologies for... Read More about Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.

Uso de herramientas online y redes sociales para diseminar investigación y estrategias de impacto social en las Artes y Humanidades: los casos de Pelibéricos y The Bigas Luna Tribute (2021)
Presentation / Conference Contribution
Fouz Hernández, S., & Gimeno Ugalde, E. (2021, January). Uso de herramientas online y redes sociales para diseminar investigación y estrategias de impacto social en las Artes y Humanidades: los casos de Pelibéricos y The Bigas Luna Tribute. Paper presented at MIAS Seminar Series 2021, Casa de Velázquez, Madrid, Spain

Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach (2020)
Presentation / Conference Contribution
Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. In SPE Virtual Europec 2020 ; proceedings (SPE-200617-MS). https://doi.org/10.2118/200617-ms

Objectives/Scope: A stable, single-well deconvolution algorithm has been introduced for well test analysis in the early 2000’s, that allows to obtain information about the reservoir system not always available from individual flow periods, for exampl... Read More about Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2021)
Presentation / Conference Contribution
Zhou, K., Cheng, Z., Shum, H. P., Li, F. W., & Liang, X. (2021). STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. . https://doi.org/10.1109/ismar52148.2021.00018

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, disloc... Read More about STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Forgotten Histories & Possible Futures: Learning from 20th century fibres and films made from waste regenerated protein sources (2020)
Presentation / Conference Contribution
Brooks, M. (2020). Forgotten Histories & Possible Futures: Learning from 20th century fibres and films made from waste regenerated protein sources. In M. Brooks, R. Blackburn, & A. Quye (Eds.), The Plastics Heritage Congress 2019 ; proceedings

This paper explores the forgotten history of fibres and films made from regenerated protein sources such as milk, soyabeans, maize, peanuts, egg-white, feathers and slaughter-house products from historical, technological and ecological perspectives.... Read More about Forgotten Histories & Possible Futures: Learning from 20th century fibres and films made from waste regenerated protein sources.

Segmentation of macular edema datasets with small residual 3D U-Net architectures (2020)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020). Segmentation of macular edema datasets with small residual 3D U-Net architectures. . https://doi.org/10.1109/bibe50027.2020.00100

This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find t... Read More about Segmentation of macular edema datasets with small residual 3D U-Net architectures.

Round table 'Descubriendo a Bigas Luna', (2020)
Presentation / Conference Contribution
Fouz-Hernández, S., Expósito, A. M., & Rua, S. G. (2020, August). Round table 'Descubriendo a Bigas Luna',. Paper presented at Descubriendo a Bigas Luna, Instituto Cervantes Sydney (online)

Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions (2019)
Presentation / Conference Contribution
Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., …Obara, B. (2019). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. In 2019 IEEE International Conference on Big Data (Big Data) (5336-5345). https://doi.org/10.1109/bigdata47090.2019.9005545

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently di... Read More about Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.