Skip to main content

Research Repository

Advanced Search

All Outputs (16)

Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery (2025)
Presentation / Conference Contribution
Gaus, Y. F. A., Isaac-Medina, B. K. S., Bhowmik, N., Lam, Y. T., & Breckon, T. P. (2025, June). Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery. Presented at 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, Tennessee, USA

Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis (2024)
Presentation / Conference Contribution
Isaac-Medina, B., Gaus, Y., Bhowmik, N., & Breckon, T. (2024, September). Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis. Presented at ECCV 2024: European Conference on Computer Vision, Milan, Italy

Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approach... Read More about Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers (2023)
Presentation / Conference Contribution
Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France

Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks... Read More about Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening (2023)
Presentation / Conference Contribution
Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection perfor... Read More about Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results (2023)
Presentation / Conference Contribution
Kiefer, B., Kristan, M., Pers, J., Zust, L., Poiesi, F., De Alcantara Andrade, F. A., Bernardino, A., Dawkins, M., Raitoharju, J., Quan, Y., Atmaca, A., Hofer, T., Zhang, Q., Xu, Y., Zhang, J., Tao, D., Sommer, L., Spraul, R., Zhao, H., Zhang, H., …Yang, M. T. (2023, January). 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results. Presented at Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023, Waikoloa, HI, USA

The 1st Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Det... Read More about 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results.

Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery (2022)
Presentation / Conference Contribution
Bhowmik, N., & Breckon, T. (2022, December). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery. Presented at International Conference on Machine Learning Applications, Bahamas

X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items,... Read More about Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery.

Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery (2022)
Presentation / Conference Contribution
Bhowmik, N., Barker, J., Gaus, Y., & Breckon, T. (2022, June). Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression c... Read More about Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery.

Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery (2022)
Presentation / Conference Contribution
Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022, June). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana

Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of th... Read More about Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery.

On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks (2021)
Presentation / Conference Contribution
Bhowmik, N., Gaus, Y., & Breckon, T. (2021, September). On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks. Presented at International Conference on Image Processing, Anchorage, AK

Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb) X-ray imagery.... Read More about On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution
Wang, Q., Bhowmik, N., & Breckon, T. (2020, December). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, Florida

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (2021)
Presentation / Conference Contribution
Thomson, W., Bhowmik, N., & Breckon, T. (2020, December). Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, FL

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bo... Read More about Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection.

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.