Outputs (4)

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.

On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks (2018)
Presentation / Conference Contribution
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

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.