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Outputs (12)

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.

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats (2020)
Journal Article
Hassan, T., Shafay, M., Akçay, S., Khan, S., Bennamoun, M., Damiani, E., & Werghi, N. (2020). Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats. Sensors, 20(22), Article 6450. https://doi.org/10.3390/s20226450

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous syste... Read More about Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer (2019)
Journal Article
Atapour-Abarghouei, A., Akcay, S., de La Garanderie, G. P., & Breckon, T. P. (2019). Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer. Pattern Recognition, 91, 232-244. https://doi.org/10.1016/j.patcog.2019.02.010

In this work, the issue of depth filling is addressed using a self-supervised feature learning model that predicts missing depth pixel values based on the context and structure of the scene. A fully-convolutional generative model is conditioned on th... Read More about Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer.

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018)
Presentation / Conference Contribution
Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. Presented at 14th Asian Conference on Computer Vision (ACCV)., Perth, Australia

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While... Read More about GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.

On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding (2018)
Presentation / Conference Contribution
Alshammari, N., Akcay, S., & Breckon, T. (2018, June). On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding. Presented at 29th IEEE Intelligent Vehicles Symposium (IEEE IV 2018)., Changshu, Suzhou, China

Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms a... Read More about On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding.

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery (2018)
Journal Article
Akcay, S., Kundegorski, M., Willcocks, C., & Breckon, T. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery. IEEE Transactions on Information Forensics and Security, 13(9), 2203-2215. https://doi.org/10.1109/tifs.2018.2812196

We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts... Read More about Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery.

Real-time Classification of Vehicle Types within Infra-red Imagery (2016)
Presentation / Conference Contribution
Kundegorski, M., Akcay, S., Payen de La Garanderie, G., Breckon, T., & Stokes, R. (2016, November). Real-time Classification of Vehicle Types within Infra-red Imagery. Presented at Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII, Edinburgh, United Kingdom

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration... Read More about Real-time Classification of Vehicle Types within Infra-red Imagery.