Outputs (211)

Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging (2021)
Journal Article
Akcay, S., & Breckon, T. (2022). Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging. Pattern Recognition, 122, Article 108245. https://doi.org/10.1016/j.patcog.2021.108245

X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising... Read More about Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging.

Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video (2021)
Journal Article
Gökstorp, S., & Breckon, T. (2022). Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video. Visual Computer, 38(6), 2033-2040. https://doi.org/10.1007/s00371-021-02264-6

Unmanned Aerial Vehicles (UAV) can be used to great effect for wide-area searches such as search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose inv... Read More about Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video.

Source Class Selection with Label Propagation for Partial Domain Adaptation (2021)
Presentation / Conference Contribution
Wang, Q., & Breckon, T. (2021, September). Source Class Selection with Label Propagation for Partial Domain Adaptation. Presented at International Conference on Image Processing, Anchorage, AK

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes a... Read More about Source Class Selection with Label Propagation for Partial Domain Adaptation.

Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation (2021)
Presentation / Conference Contribution
Alshammari, N., Akcay, S., & Breckon, T. (2021, July). Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation. Presented at IEEE Intelligent Transportation Systems Society

Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather),... Read More about Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation.

Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation (2021)
Presentation / Conference Contribution
Alshammari, N., Akcay, S., & Breckon, T. (2021, July). Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation. Presented at 2021 IEEE Intelligent Vehicles Symposium (IV 2021), Nagoya, Japan

— Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are app... Read More about Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021, January). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI (2021)
Presentation / Conference Contribution
Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., & Breckon, T. (2021, January). Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) has provided a non-invasive method of interf... Read More about Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.

On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures (2021)
Presentation / Conference Contribution
Poyser, M., Atapour-Abarghouei, A., & Breckon, T. (2021, January). On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. Presented at 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks. Whilst the reported performance of these a... Read More about On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures.

Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery (2021)
Presentation / Conference Contribution
Isaac-Medina, B., Willcocks, C., & Breckon, T. (2021, January). Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the vie... Read More about Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery.

Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery (2021)
Presentation / Conference Contribution
Sasaki, H., Willcocks, C., & Breckon, T. (2021, January). Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limit... Read More about Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery.