All Outputs (9)

Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset (2023)
Conference Proceeding
Wu, Z., Moemeni, A., Caleb-Solly, P., & Castle-Green, S. (2023). Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset. In 2023 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn54540.2023.10191368

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in re... Read More about Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset.

Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation (2023)
Conference Proceeding
Chen, J., Moemeni, A., & Caleb-Solly, P. (2023). Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation. In HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (644-648). https://doi.org/10.1145/3568294.3580165

Robot teleoperation is being explored in a number of application areas, where combining human adaptive intelligence and high precision of robots can provide access to dangerous or inaccessible places, or augment human dexterity. Using virtual reality... Read More about Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation.

Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning (2021)
Conference Proceeding
Calderon-Ramırez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Calvo-Valverde, L., Yang, S., Moemeni, A., …Molina-Cabello, M. (2021). Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning. . https://doi.org/10.1109/IJCNN52387.2021.9533719

Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenari... Read More about Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning.

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images (2021)
Conference Proceeding
Calderon-Ramirez, S., Giri, R., Moemeni, A., Umaña, M., Elizondo, D., Torrents-Barrena, J., & Molina-Cabello, M. A. (2021). Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. In Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition. https://doi.org/10.1109/ICPR48806.2021.9412946

Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently availabl... Read More about Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images.

Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning (2020)
Conference Proceeding
Bermudez, A., Calderon-Ramirez, S., Thang, T., Tyrrell, P., Moemeni, A., Yang, S., & Torrents-Barrena, J. (2020). Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning. . https://doi.org/10.1109/IJCNN48605.2020.9206779

Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based... Read More about Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning.

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data (2020)
Conference Proceeding
Rostami-Shahrbabaki, M., Bogenberger, K., Safavi, A. A., & Moemeni, A. (2020). Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

Current traffic management systems in urban networks require real-time estimation of the traffic states.With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement... Read More about Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data.

Low-Cost Automatic Ambient Assisted Living System (2018)
Conference Proceeding
Malekmohamad, H., Moemeni, A., Orun, A., & Purohit, J. K. (2018). Low-Cost Automatic Ambient Assisted Living System. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (693-697). https://doi.org/10.1109/PERCOMW.2018.8480390

The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, prot... Read More about Low-Cost Automatic Ambient Assisted Living System.

Inertial-visual pose tracking using optical flow-aided particle filtering (2014)
Conference Proceeding
Moemeni, A., & Tatham, E. (2014). Inertial-visual pose tracking using optical flow-aided particle filtering. In 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP). https://doi.org/10.1109/cimsivp.2014.7013296

This paper proposes an algorithm for visual-inertial camera pose tracking, using adaptive recursive particle filtering. The method benefits from the agility of inertial-based and robustness of vision-based tracking. A proposal distribution has been d... Read More about Inertial-visual pose tracking using optical flow-aided particle filtering.

A framework for camera pose tracking using stochastic data fusion (2010)
Conference Proceeding
Moemeni, A., & Tatham, E. (2010). A framework for camera pose tracking using stochastic data fusion. In 2010 2nd International IEEE Consumer Electronics Society's Games Innovations Conference. https://doi.org/10.1109/icegic.2010.5716876

A novel camera pose tracking system using a stochastic inertial-visual sensor fusion has been proposed. A method based on the Particle Filtering concept has been adapted for inertial and vision data fusion, which benefits from the agility of inertial... Read More about A framework for camera pose tracking using stochastic data fusion.