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Outputs (205)

Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery (2020)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Isaac-Medina, B., & Breckon, T. (2020, September). Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery. Presented at Spie Security + Defence

Object detection from infrared-band (thermal) imagery has been a challenging problem for many years. With the advent of deep Convolutional Neural Networks (CNN), the automated detection and classification of objects of interest within the scene has b... Read More about Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery.

Domain Adaptation via Image Style Transfer (2020)
Book Chapter
Atapour-Abarghouei, A., & Breckon, T. (2020). Domain Adaptation via Image Style Transfer. In H. Venkateswara, & S. Panchanathan (Eds.), Domain adaptation in computer vision with deep learning (137-156). Springer Verlag. https://doi.org/10.1007/978-3-030-45529-3_8

While recent growth in modern machine learning techniques has led to remarkable strides in computer vision applications, one of the most significant challenges facing learning-based vision systems is the scarcity of large, high-fidelity datasets requ... Read More about Domain Adaptation via Image Style Transfer.

A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes (2020)
Journal Article
Wang, Q., Megherbi, N., & Breckon, T. (2020). A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 28(3), 507-526. https://doi.org/10.3233/xst-200654

BACKGROUND: Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner. It has been shown to be highl... Read More about A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes.

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling (2020)
Presentation / Conference Contribution
Wang, Q., & Breckon, T. (2020, February). Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling. Presented at Thirty Fourth AAAI Conference on Artificial Intelligence, New York, USA

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a re... Read More about Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling.

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.

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.

On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding (2019)
Presentation / Conference Contribution
Ismail, K., & Breckon, T. (2019, December). On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles)... Read More about On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding.

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.

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.