Outputs (211)

On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery (2019)
Presentation / Conference Contribution
Bhowmik, N., Gaus, Y., Akcay, S., Barker, J., & Breckon, T. (2019, December). On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anom... Read More about On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery.

Region Based Anomaly Detection With Real-Time Training and Analysis (2019)
Presentation / Conference Contribution
Adey, P., Bordewich, M., Breckon, T., & Hamilton, O. (2019, December). Region Based Anomaly Detection With Real-Time Training and Analysis. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region... Read More about Region Based Anomaly Detection With Real-Time Training and Analysis.

Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery (2019)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Akcay, S., & Breckon, T. (2019, December). Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives wit... Read More about Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery.

Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items (2019)
Presentation / Conference Contribution
Bhowmik, N., Gaus, Y., & Breckon, T. (2019, November). Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items. Presented at 2019 IEEE International Symposium on Technologies for Homeland Security, Boston, USA

X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of part... Read More about Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items.

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (2019)
Presentation / Conference Contribution
Samarth, G., Bhowmik, N., & Breckon, T. (2019, December). Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced co... Read More about Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection.

An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening (2019)
Journal Article
Wang, Q., Ismail, K., & Breckon, T. (2020). An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 28(1), 35-58. https://doi.org/10.3233/xst-190531

BACKGROUND: The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviati... Read More about An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening.

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery (2019)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., & Breckon, T. (2019, November). On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery. Presented at 2019 IEEE International Symposium on Technologies for Homeland Security, Boston, USA

X-ray imagery security screening is essential to maintaining transport security against a varying profile of prohibited items. Particular interest lies in the automatic detection and classification of prohibited items such as firearms and firearm com... Read More about On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery.

Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation (2019)
Book Chapter
Atapour-Abarghouei, A., & Breckon, T. (2019). Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation. In P. L. Rosin, Y. Lai, L. Shao, & Y. Liu (Eds.), RGB-D image analysis and processing (15-50). Springer Verlag. https://doi.org/10.1007/978-3-030-28603-3_2

Even though obtaining 3D information has received significant attention in scene capture systems in recent years, there are currently numerous challenges within scene depth estimation which is one of the fundamental parts of any 3D vision system focu... Read More about Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation.

DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation (2019)
Presentation / Conference Contribution
Stephenson, F., Breckon, T., & Katramados, I. (2019, September). DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulti... Read More about DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation.

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019)
Presentation / Conference Contribution
Akcay, A., Atapour-Abarghouei, A., & Breckon, T. P. (2019, July). Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection. Presented at Proc. Int. Joint Conference on Neural Networks, Budapest, Hungary

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data... Read More about Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection.