Staff profile

Samet Akcay is a third year Ph.D. Student in the Department of Computer Science at Durham University, UK. He received his MSc degree from the Department of Electrical Engineering at Penn State University, USA. His primary research interests are real-time image classification/detection, anomaly detection, unsupervised feature learning via deep/machine learning algorithms.

Education

• PhD - Durham University, UK, 2015 - 2019 (Expected)
• MSc - Penn State University, US, 2013-2015
• BSc - Gazi University, TR, 2007-2011

• Real Time Object Detection
• Deep Learning
• Machine Learning
• Computer Vision

Conference Paper

• Yucer, S., Akcay, S., Al Moubayed, N., & Breckon, T. (2020, June). Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation. Presented at Computer Vision and Pattern Recognition Workshops, Seattle, USA
• 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
• 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
• Guo, T., Akcay, S., Adey, P., & Breckon, T. (2018, October). On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece
• Akcay, S., & Breckon, T. (2017, December). An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery. Presented at IEEE International Conference on Image Processing (ICIP), Beijing, China
• 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
• Kundegorski, M., Akcay, S., Devereux, M., Mouton, A., & Breckon, T. (2016, January). On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening. Presented at International Conference on Imaging for Crime Detection and Prevention, Madrid, Spain
• Akcay, S., Kundegorski, M., Devereux, M., & Breckon, T. (2016, September). Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA

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
• 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
• 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
• 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