Staff profile

Toby Breckon is a Professor in the Department of Computer Science and Department of Engineering at Durham University and an academic tutor at St. Chads College.

Within the department(s), he leads research in computer vision, image processing and robotic sensing, with a strong emphasis on AI-based deep machine learning and pattern recognition techniques, in addition to research-led teaching within the undergraduate Engineering and Computer Science  programmes.

Research Interests: computer vision, deep learning, robotic sensing, brain computer interfaces (BCI), 3D scene understanding, real-time computer vision, vehicle perception systems, autonomous vehicles.

PhD Applicants: if you are interested in joining our research team please see details here, some potential projects/topics here and email me to discuss. 

Experience

Prof. Breckon's current research spans a breadth of computer vision, image processing and robotic sensing application domains including automotive sensing, X-ray security image understanding, automated visual surveillance and robotics.

Within automotive, his team work with a number of major vehicle manufacturers on future automotive sensing solutions (2011- 2024) having originally commenced work in this area in the early days of intelligent driver assistance systems (2007-2010). The team's work on real-time visual saliency was filed as a patent (2013) and Prof. Breckon acted as a scientific advisor to tech startup Machines With Vision on aspects of autonomous vehicle sensing (2019-2023).

Within aviation security, his research work on X-ray image understanding pioneered the use of automated prohibited item detection algorithms within the sector and his team are credited with designing the first complete solution for threat image projection (TIP) within 3D CT security scan imagery (E&T Innovation Awards 2020, Highly Commended, Dynamites Technology Awards 2021, Innovator of the Year - Highly Commended). Their 3D TIP approach is now used globally by several major security scanner manufacturers, in numerous major international airports, and helps to secure over 500+ million passenger journeys per annum across five continents (20

The work of his team on anomaly detection was used by COSMONiO in their NOUS product. COSMONiO, founded by former members of his research team, was acquired by Intel in 2020.

His team were a collaborator in the original UK SAPIENT programme, and developed an infrared (thermal) based autonomous sensor unit to demonstrate 'the art of the possible' in inter-operable AI for multi-sensor wide area surveillance. As of 2023, SAPIENT is a British Standard (BSI Flex 355) and the UK MoD inter-operabilty standard for counter-UAS (uncrewed air system) technology.

In collaboration with Blue Bear Systems, work from his team directly supported the development of intelligent payloads for "the largest collaborative, military focused evaluation of swarming uncrewed aerial vehicles (UAV) in the UK"  (2021). Furthermore, he has acted as a technical consultant on a wide range of industry-led projects, supporting the development of several commercial products (2013- 2024), and as an expert technical witness in US Federal Court (2021).

The broader international reach of his research is further chronicled in three research impact case studies submitted as part of the UK National Research Evaluation Framework (REF) spanning work on X-ray security imaging, automotive sensing and wide-area visual surveillance (2020/21) and he is the recipient of the Durham University Award for Excellence in Knowledge Transfer in recognition of his outstanding contribution to the public benefit of research (2022).

In the early part of his research career, he led the technical development of real-time object detection for the Stellar Team's SATURN multi-platform robot system in the MoD Grand Challenge, going on to win the R.J. Mitchell Trophy (UK MoD Grand Challenge winners, 2008), the Finmeccanica Group Innovation Award (2009) and an IET Award for Innovation (Team Category, 2009).

His research work is recognised by the Royal Photographic Society Selwyn Award (2011) for a significant early career contribution to imaging science.

Background

Before joining Durham in 2013, he held faculty positions at the School of Engineering, Cranfield University, the UK's only postgraduate-only university, and the School of Informatics, University of Edinburgh. Prior to this he was a mobile robotics research engineer with the UK MoD (DERA) and latterly QinetiQ in addition to prior positions with the schools inspectorate OFSTED, the Scottish Language Dictionaries organisation and (dot-com) software house Orbital Software.

He has held visiting faculty positions at ESTIA (Ecole Supérieure des Technologies Industrielles Avancées), South-West France, Northwestern Polytechnical University (Xi'an, China), Waseda University (Kitakyushu, Japan) and Shanghai Jiao Tong University (Shanghai, China).

He holds a PhD in Informatics (Artificial Intelligence - Computer Vision) from the University of Edinburgh and studied Artificial Intelligence and Computer Science as an undergraduate (B.Sc. (Hons.) (Edin.)).

Service and Outreach

Prof. Breckon is a consultant scientific advisor to the UK Dept of Transport, as a member of the DfT College of Experts (2023+), and has previously served as a scientific advisor to H.M. Cabinet Office (Cyber Security Expert Group, 2015-2020) and previously to H.M. Government Office for Science (2016/17).

At Durham, Prof. Breckon led applied Computer Science research, as Head of Innovative Computing within the School of Engineering and Computing Science (2014-2018) and now leads research spanning the visual computing theme as Head of VIViD (Vision, Imaging and Visualisation in Durham, 2021-present) in the Department of Computer Science. From 2020, he serves as a member of the Ethics Advisory Committee bringing broad experience in the application of ethics approval and practice within Artificial Intelligence and related areas.

From 2023, he is the option leader for the MSc specialist option in Computer Vision and Robotics available as part of the MSc in Scientific Computing and Data Analysis (MISCADA) at Durham

He is a member of the executive committee of the BMVA (British Machine Vision Association) acting as Treasurer for financial oversight of the association's annual computer vision conferences (BMVC, MIUA), summer school and other activities (2010-present).

Outside of the university, he acts as a STEMNET Science & Engineering Ambassador promoting awareness of intelligent sensing, its underpinning technology and related societal impact.

• computer vision
• machine learning
• robotic sensing
• autonomous systems

Authored book

• Dictionary of Computer Vision and Image Processing
  Fisher, R., Breckon, T., Dawson-Howe, K., Fitzgibbon, A., Robertson, C., Trucco, E., & Williams, C. (2014). Dictionary of Computer Vision and Image Processing. (2nd). Wiley
• Fundamentos de Processamento Digital de Imagens - Uma Abordagem Pratica com Exemplos em Matlab
  Solomon, C., & Breckon, T. (2013). Fundamentos de Processamento Digital de Imagens - Uma Abordagem Pratica com Exemplos em Matlab. LTC
• Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
  Solomon, C., & Breckon, T. (2010). Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley. https://doi.org/10.1002/9780470689776

Chapter in book

• Domain Adaptation via Image Style Transfer
  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
• Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation
  Atapour-Abarghouei, A., & Breckon, T. (2019). Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation. In P. L. Rosin, Y.-K. 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

Conference Paper

• Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
  Isaac-Medina, B., & Breckon, T. (2025, June). Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection. Presented at Computer Vision Pattern Recognition Workshops, Nashville, Tennessee, USA
• DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
  Sun, Y., Li, L., E, W., Atapour-Abarghouei, A., & Breckon, T. (2025, June). DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions. Presented at International Joint Conference on Neural Networks, Rome, Italy
• Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery
  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
  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
• RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
  Li, L., Shum, H. P. H., & Breckon, T. P. (2024, September). RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation. Presented at ECCV 2024: European Conference on Computer Vision, Milan, Italy
• Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
  Gaus, Y. F. A., Bhowmik, N., Isaac-Medina, B. K. S., & Breckon, T. P. (2024, June). Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA
• Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy
  Rafiei, M., Breckon, T. P., & Iosifidis, A. (2024, June). Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan
• Extracting Quantitative Streamline Information from Surface Flow Visualization Images in a Linear Cascade using Convolutional Neural Networks
  Liu, X., Ingram, G., Sims-Williams, D., & Breckon, T. P. (2024, September). Extracting Quantitative Streamline Information from Surface Flow Visualization Images in a Linear Cascade using Convolutional Neural Networks. Presented at GPPS Chania24, Chania
• Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
  Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024, June). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan
• U3DS3 : Unsupervised 3D Semantic Scene Segmentation
  Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024, January). U3DS3 : Unsupervised 3D Semantic Scene Segmentation. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA
• TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training
  Li, L., Qiao, T., Shum, H. P. H., & Breckon, T. P. (2024, November). TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training. Presented at BMVC'24: The 35th British Machine Vision Conference, Glasgow, UK
• On Fine-tuned Deep Features for Unsupervised Domain Adaptation
  Wang, Q., Meng, F., & Breckon, T. (2023, June). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. Presented at IJCNN 2023: International Joint Conference on Neural Networks, Queensland, Australia
• Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
  Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023, June). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC
• Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
  Li, L., Shum, H. P., & Breckon, T. P. (2023, June). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. Presented at 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Vancouver, BC
• Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening
  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
• ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction
  Yu, Z., Haung, S., Fang, C., Breckon, T., & Wang, J. (2023, June). ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC
• Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
  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
• Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
  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
• Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
  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
• Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery
  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
• VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification
  Alsehaim, A., & Breckon, T. (2022, November). VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification. Presented at BMVC 2022: The 33rd British Machine Vision Conference, London, UK
• Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
  Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T., & Willcocks, C. (2022, October). Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. Presented at ECCV 2022: European Conference on Computer Vision, Tel Aviv, Israel
• Does lossy image compression affect racial bias within face recognition?
  Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022, October). Does lossy image compression affect racial bias within face recognition?. Presented at International Joint Conference on Biometrics (IJCB 2022), Abu Dhabi, UAE
• Multi-view Vision Transformers for Object Detection
  Isaac-Medina, B., Willcocks, C., & Breckon, T. (2022, August). Multi-view Vision Transformers for Object Detection. Presented at International Conference on Pattern Recognition, Montreal, Canada
• On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision
  Groom, M., & Breckon, T. (2022, August). On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision. Presented at 26th International Conference on Pattern Recognition, Montreal, Québec
• Evaluating Gaussian Grasp Maps for Generative Grasping Models
  Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022, July). Evaluating Gaussian Grasp Maps for Generative Grasping Models. Presented at Proc. Int. Joint Conf. Neural Networks, Padova, Italy
• Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery
  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
• Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery
  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
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery
  Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. Presented at 2022 17th International Conference on Computer Vision Theory and Applications
• Measuring Hidden Bias within Face Recognition via Racial Phenotypes
  Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. (2022, January). Measuring Hidden Bias within Face Recognition via Racial Phenotypes. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI
• Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery
  Barker, J., Bhowmik, N., & Breckon, T. (2022, February). Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery. Presented at Computer Vision Theory and Applications 2022
• Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention
  Raju, J., Gaus, Y., & Breckon, T. (2021, December). Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention. Presented at 20th IEEE International Conference on Machine Learning Applications
• Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
  Wang, Q., & Breckon, T. (2021, December). Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 20th IEEE International Conference on Machine Learning Applications
• Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery
  Webb, T., Bhowmik, N., Gaus, Y., & Breckon, T. (2021, December). Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery. Presented at 20th IEEE International Conference on Machine Learning Applications
• DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications
  Li, L., Ismail, K. N., Shum, H. P., & Breckon, T. P. (2021, December). DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. Presented at International Conference on 3D Vision, Surrey / Online
• Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition
  Alsehaim, A., & Breckon, T. (2021, November). Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition. Presented at BMVC 2021, Online
• Source Class Selection with Label Propagation for Partial Domain Adaptation
  Wang, Q., & Breckon, T. (2021, September). Source Class Selection with Label Propagation for Partial Domain Adaptation. Presented at International Conference on Image Processing, Anchorage, AK
• On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks
  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
• Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation
  Alshammari, N., Akcay, S., & Breckon, T. (2021, July). Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation. Presented at IEEE Intelligent Transportation Systems Society
• Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation
  Alshammari, N., Akcay, S., & Breckon, T. (2021, July). Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation. Presented at 2021 IEEE Intelligent Vehicles Symposium (IV 2021), Nagoya, Japan
• On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures
  Poyser, M., Atapour-Abarghouei, A., & Breckon, T. (2021, January). On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. Presented at 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy
• Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
  Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021, October). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. Presented at 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada
• Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
  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
• Autoencoders Without Reconstruction for Textural Anomaly Detection
  Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021, July). Autoencoders Without Reconstruction for Textural Anomaly Detection. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China
• Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery
  Isaac-Medina, B., Willcocks, C., & Breckon, T. (2021, January). Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• PANDA: Perceptually Aware Neural Detection of Anomalies
  Barker, J., & Breckon, T. (2021, July). PANDA: Perceptually Aware Neural Detection of Anomalies. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China
• Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
  Sasaki, H., Willcocks, C., & Breckon, T. (2021, January). Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI
  Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., & Breckon, T. (2021, January). Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss
  Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021, January). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening
  Wang, Q., & Breckon, T. (2021, July). On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening. Presented at 2021 International Joint Conference on Neural Networks (IJCNN)
• Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
  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
• On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery
  Wang, Q., Bhowmik, N., & Breckon, T. (2020, July). On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at International Joint Conference on Neural Networks, Glasgow, Scotland
• Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery
  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
• Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification
  Alsehaim, A., & Breckon, T. (2021, January). Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification. Presented at 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy
• Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
  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
• Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
  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
• Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
  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
• On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery
  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
• Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
  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
• The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery
  Bhowmik, N., Wang, Q., Gaus, Y., Szarek, M., & Breckon, T. (2019, September). The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery. Presented at British Machine Vision Conference Workshops, Cardiff, Wales, UK
• To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation
  Atapour-Abarghouei, A., & Breckon, T. P. (2019, September). To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation. Presented at International Conference on 3D Vision, Quebec
• Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior
  Atapour-Abarghouei, A., & Breckon, T. (2019, September). Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior. Presented at IEEE International Conference on Image Processing, Taipei, Taiwen
• A Ranking based Attention Approach for Visual Tracking
  Peng, S., Kamata, S., & Breckon, T. (2019, September). A Ranking based Attention Approach for Visual Tracking. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan
• Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation
  Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, May). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. Presented at 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada
• Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
  Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, December). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. Presented at International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary
• Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach
  Atapour-Abarghouei, A., & Breckon, T. (2019, June). Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, California, USA
• Region Based Anomaly Detection With Real-Time Training and Analysis
  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
• Style Augmentation: Data Augmentation via Style Randomization
  Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019, June). Style Augmentation: Data Augmentation via Style Randomization. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision, Long Beach, CA, USA
• A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
  Wang, Q., Ning, J., & Breckon, T. (2019, September). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan
• Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items
  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
• Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
  Wang, Q., Bu, P., & Breckon, T. (2019, May). Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition. Presented at Proc. International Joint Conference on Neural Networks, Budapest
• On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery
  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
• Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
  Gaus, Y., Bhowmik, N., Akcay, A., Guillen-Garcia, P., Barker, J., & Breckon, T. (2019, July). Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery. Presented at Proc. Int. Joint Conference on Neural Networks, Budapest, Hungary
• On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding
  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
• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
  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
• DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation
  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
• Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
  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
• Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
  Payen de La Garanderie, G., Atapour-Abarghouei, A., & Breckon, T. (2018, September). Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. Presented at 15th European Conference on Computer Vision (ECCV 2018), Munich, Germany
• Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion
  Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion. Presented at International Conference Image Analysis and Recognition, Póvoa de Varzim, Portugal
• On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding
  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 The 29th IEEE Intelligent Vehicles Symposium (IEEE IV 2018)., Changshu, China
• Extended Patch Prioritization For Depth Hole Filling Within Constrained Exemplar-Based RGB-D Image Completion
  Atapour-Abarghouei, A., & Breckon, T. (2018, June). Extended Patch Prioritization For Depth Hole Filling Within Constrained Exemplar-Based RGB-D Image Completion. Presented at 15th International Conference on Image Analysis and Recognition (ICIAR 2018)., Póvoa de Varzim, Portugal
• Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction
  Holder, C., & Breckon, T. (2018, June). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction. Presented at The 29th Intelligent Vehicles Symposium (IEEE IV 2018)., Changshu, China
• Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments
  Holder, C., & Breckon, T. (2018, June). Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments. Presented at 15th International Conference on Image Analysis and Recognition (ICIAR 2018)., Póvoa de Varzim, Portugal
• Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras
  Lin, K., & Breckon, T. (2018, June). Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras. Presented at 15th International Conference on Image Analysis and Recognition (ICIAR 2018)., Póvoa de Varzim, Portugal
• Author profiling: Gender prediction from Tweets and images: Notebook for PAN at CLEF 2018
  HaCohen-Kerner, Y., Yigal, Y., Shayovitz, E., Miller, D., & Breckon, T. (2018, October). Author profiling: Gender prediction from Tweets and images: Notebook for PAN at CLEF 2018. Presented at CEUR Workshop Proceedings, Torino, Italy
• On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding
  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
• On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling
  Loveday, M., & Breckon, T. (2018, June). On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling. Presented at Computer Vision and Pattern Recognition Workshops (CVPR) 2018., Salt Lake City, Utah, USA
• Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection
  Dunnings, A., & Breckon, T. (2018, October). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece
• Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy
  Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018, July). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. Presented at 19th Towards Autonomous Robotic Systems (TAROS) Conference., Bristol, England
• Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer
  Atapour-Abarghouei, A., & Breckon, T. (2018, June). Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer. Presented at 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., Salt Lake City, Utah, USA
• Infrared Image Colorization Using S-Shape Network
  Dong, Z., Kamata, S., & Breckon, T. (2018, October). Infrared Image Colorization Using S-Shape Network. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece
• On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks
  Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018, October). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan
• On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks
  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
• An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery
  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
• DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation
  Atapour-Abarghouei, A., & Breckon, T. (2017, September). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation. Presented at 28th British Machine Vision Conference (BMVC) 2017, London, UK
• An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy
  Maciel-Pearson, B., & Breckon, T. (2017, December). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. Presented at The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us., Bristol, England
• Face Recognition via Deep Sparse Graph Neural Networks
  Wu, R., Kamata, S., & Breckon, T. (2017, September). Face Recognition via Deep Sparse Graph Neural Networks. Presented at 28th British Machine Vision Conference (BMVC) 2017., London, UK
• From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes
  Holder, C., Breckon, T., & Wei, X. (2016, December). From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes. Presented at European Conference on Computer Vision Workshops., Amsterdam, The Netherlands
• SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
  Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
• Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)
  Thomas, P., Marshall, G., Faulkner, D., Kent, P., Page, S., Islip, S., Oldfield, J., Breckon, T., Kundegorski, M., Clarke, D., & Styles, T. (2016, April). Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR). Presented at SPIE Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent Intelligence Surveillance and Reconnaissance VII, Baltimore, Maryland, USA
• Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery
  Atapour-Abarghouei, A., de La Garanderie, G. P., & Breckon, T. P. (2016, December). Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery. Presented at 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun
• Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow
  Hamilton, O., & Breckon, T. (2016, September). Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA
• Real-time Classification of Vehicle Types within Infra-red Imagery
  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
• Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery
  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
• Constant-time Bilateral Filter using Spectral Decomposition
  Sugimoto, K., Breckon, T., & Kamata, S. (2016, September). Constant-time Bilateral Filter using Spectral Decomposition. Presented at 2016 IEEE International Conference on Image Processing (ICIP)., Phoenix, AZ, USA
• Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications
  Katramados, I., & Breckon, T. (2016, September). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA
• On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening
  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
• Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots
  Cavestany, P., Rodríguez, A., Rodriguez, A., Martínez-Barberá, H., Martinez-Barbera, H., & Breckon, T. (2015, September). Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots. Presented at Proceedings of IEEE International Conference on Image Processing, Québec City, Canada
• Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation
  Webster, D., & Breckon, T. (2015, September). Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation. Presented at Proceedings of IEEE International Conference on Image Processing, Québec City, Canada
• Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery
  Kundegorski, M., & Breckon, T. (2015, September). Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery. Presented at Optics and Photonics for Counterterrorism, Crime Fighting and Defence, Toulouse, France
• Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo
  Payen de La Garanderie, G., & Breckon, T. (2014, September). Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo. Presented at Proceedings of the British Machine Vision Conference
• A Photogrammetric Approach for Real-time 3D Localization and Tracking of Pedestrians in Monocular Infrared Imagery
  Kundegorski, M., & Breckon, T. (2014, October). A Photogrammetric Approach for Real-time 3D Localization and Tracking of Pedestrians in Monocular Infrared Imagery. Presented at Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence
• Using Compressed Audio-visual Words for Multi-modal Scene Classification
  Kurcius, J., & Breckon, T. (2014, November). Using Compressed Audio-visual Words for Multi-modal Scene Classification. Presented at Proc. International Workshop on Computational Intelligence for Multimedia Understanding
• A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions
  Walger, D., Breckon, T., Gaszczak, A., & Popham, T. (2014, November). A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions. Presented at Proc. International Workshop on Computational Intelligence for Multimedia Understanding
• 3D Object Classification in Baggage Computed Tomography Imagery using Randomised Clustering Forests
  Mouton, A., Breckon, T., Flitton, G., & Megherbi, N. (2014, October). 3D Object Classification in Baggage Computed Tomography Imagery using Randomised Clustering Forests. Presented at Proc. International Conference on Image Processing
• Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection
  Megherbi, N., Breckon, T., Flitton, G., & Mouton, A. (2013, October). Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection. Presented at Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence
• Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection
  Megherbi, N., Breckon, T., & Flitton, G. (2013, October). Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection. Presented at Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence
• A Distance Weighted Method for Metal Artefact Reduction in CT
  Mouton, A., Megherbi, N., Breckon, T., Van Slambrouck, K., & Nuyts, J. (2013, September). A Distance Weighted Method for Metal Artefact Reduction in CT. Presented at Proc. International Conference on Image Processing
• An evaluation of image denoising techniques applied to CT baggage screening imagery
  Mouton, A., Flitton, G. T., Bizot, S., Megherbi, N., & Breckon, T. P. (2013, July). An evaluation of image denoising techniques applied to CT baggage screening imagery. Presented at Proceedings of the IEEE International Conference on Industrial Technology, Cape Town, South Africa
• A Foreground Object based Quantitative Assessment of Dense Stereo Approaches for use in Automotive Environments
  Hamilton, O., Breckon, T., Bai, X., & Kamata, S. (2013, September). A Foreground Object based Quantitative Assessment of Dense Stereo Approaches for use in Automotive Environments. Presented at Proc. International Conference on Image Processing
• Robust Motion Filtering as an Enabler to Video Stabilization for a Tele-operated Mobile Robot
  Chereau, R., & Breckon, T. (2013, September). Robust Motion Filtering as an Enabler to Video Stabilization for a Tele-operated Mobile Robot. Presented at Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII
• Challenges of Finding Aesthetically Pleasing Images
  Faria, J., Bagley, S., Rueger, S., & Breckon, T. (2013, July). Challenges of Finding Aesthetically Pleasing Images. Presented at Proc. International Workshop on Image and Audio Analysis for Multimedia Interactive Services
• Image Super-Resolution Applied to Moving Targets in High Dynamics Scenes
  Mise, O., & Breckon, T. (2013, September). Image Super-Resolution Applied to Moving Targets in High Dynamics Scenes. Presented at Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems
• Gabor Features for Real-Time Road Environment Classification
  Mioulet, L., Breckon, T., Mouton, A., Liang, H., & Morie, T. (2013, February). Gabor Features for Real-Time Road Environment Classification. Presented at Proc. International Conference on Industrial Technology
• Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words
  Turcsany, D., Mouton, A., & Breckon, T. (2013, February). Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words. Presented at Proc. International Conference on Industrial Technology
• Human Pose Classification within the Context of Near-IR Imagery Tracking
  Han, J., Gaszczak, A., Maciol, R., Barnes, S., & Breckon, T. (2013, September). Human Pose Classification within the Context of Near-IR Imagery Tracking. Presented at Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence
• Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance
  Breckon, T., Gaszczak, A., Han, J., Eichner, M., & Barnes, S. (2013, September). Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance. Presented at Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems
• Fully Automatic 3D Threat Image Projection: Application to Densely Cluttered 3D Computed Tomography Baggage Images
  Megherbi, N., Breckon, T., Flitton, G., & Mouton, A. (2012, October). Fully Automatic 3D Threat Image Projection: Application to Densely Cluttered 3D Computed Tomography Baggage Images. Presented at Proc. International Conference on Image Processing Theory, Tools and Applications
• A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening
  Megherbi, N., Han, J., Flitton, G., & Breckon, T. (2012, September). A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening. Presented at Proc. International Conference on Image Processing
• A Novel Intensity Limiting Approach to Metal Artefact Reduction in 3D CT Baggage Imagery
  Mouton, A., Megherbi, N., Flitton, G., Bizot, S., & Breckon, T. (2012, September). A Novel Intensity Limiting Approach to Metal Artefact Reduction in 3D CT Baggage Imagery. Presented at Proc. International Conference on Image Processing
• A 3D extension to cortex like mechanisms for 3D object class recognition
  Flitton, G., Breckon, T., & Megherbi, N. (2012, December). A 3D extension to cortex like mechanisms for 3D object class recognition. Presented at 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA
• Correlating Histology and Spectroscopy to Differentiate Pathologies of the Colon
  Carey, D., Shepherd, N., Kendall, C., Stone, N., Breckon, T., & Lloyd, G. (2012, July). Correlating Histology and Spectroscopy to Differentiate Pathologies of the Colon. Presented at Proc. Conference on Medical Image Understanding and Analysis
• On Cross-Spectral Stereo Matching using Dense Gradient Features
  Pinggera, P., Breckon, T., & Bischof, H. (2012, September). On Cross-Spectral Stereo Matching using Dense Gradient Features. Presented at Proc. British Machine Vision Conference
• Consistency in Muti-modal Automated Target Detection using Temporally Filtered Reporting
  Breckon, T., Han, J., & Richardson, J. (2012, November). Consistency in Muti-modal Automated Target Detection using Temporally Filtered Reporting. Presented at Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI
• Real-time People and Vehicle Detection from UAV Imagery
  Gaszczak, A., Breckon, T., & Han, J. (2011, December). Real-time People and Vehicle Detection from UAV Imagery. Presented at Proc. SPIE Conference Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques
• Adaptive Object Placement for Augmented Reality Use in Driver Assistance Systems
  Bordes, L., Breckon, T., Katramados, I., & Kheyrollahi, A. (2011, November). Adaptive Object Placement for Augmented Reality Use in Driver Assistance Systems. Presented at Proc. 8th European Conference on Visual Media Production
• Real-time Mosaicing from Unconstrained Video Imagery for UAV Applications
  Breszcz, M., Breckon, T., & Cowling, I. (2011, April). Real-time Mosaicing from Unconstrained Video Imagery for UAV Applications. Presented at Proc. 26th International Conference on Unmanned Air Vehicle Systems
• Video Re-sampling and Content Re-targeting for Realistic Driving Incident Simulation
  Heras, A., Breckon, T., & Tirovic, M. (2011, November). Video Re-sampling and Content Re-targeting for Realistic Driving Incident Simulation. Presented at Proc. 8th European Conference on Visual Media Production
• Real-time Visual Saliency by Division of Gaussians
  Katramados, I., & Breckon, T. (2011, September). Real-time Visual Saliency by Division of Gaussians. Presented at Proc. International Conference on Image Processing
• A Non-temporal Texture Driven Approach to Real-time Fire Detection
  Chenebert, A., Breckon, T., & Gaszczak, A. (2011, September). A Non-temporal Texture Driven Approach to Real-time Fire Detection. Presented at Proc. International Conference on Image Processing
• A Classifier based Approach for the Detection of Potential Threats in CT based Baggage Screening
  Megherbi, N., Flitton, G., & Breckon, T. (2010, September). A Classifier based Approach for the Detection of Potential Threats in CT based Baggage Screening. Presented at Proc. International Conference on Image Processing
• Object Recognition using 3D SIFT in Complex CT Volumes
  Flitton, G., Breckon, T., & Megherbi, N. (2010, September). Object Recognition using 3D SIFT in Complex CT Volumes. Presented at Proc. British Machine Vision Conference
• Automatic Salient Object Detection in UAV Imagery
  Sokalski, J., Breckon, T., & Cowling, I. (2010, April). Automatic Salient Object Detection in UAV Imagery. Presented at Proc. 25th International Conference on Unmanned Air Vehicle Systems
• Automatic Road Feature Detection and Correlation for the Correction of Consumer Satellite Navigation System Mapping
  Kowaliszyn, M., & Breckon, T. (2010, May). Automatic Road Feature Detection and Correlation for the Correction of Consumer Satellite Navigation System Mapping. Presented at Proc. IET/ITS Conference on Road Transport Information and Control
• Autonomous Real-time Vehicle Detection from a Medium-Level UAV
  Breckon, T., Barnes, S., Eichner, M., & Wahren, K. (2009, March). Autonomous Real-time Vehicle Detection from a Medium-Level UAV. Presented at Proc. 24th International Conference on Unmanned Air Vehicle Systems
• Development of a Two-Tier Unmanned Air System for the MoD Grand Challenge
  Wahren, K., Cowling, I., Patel, Y., Smith, P., & Breckon, T. (2009, March). Development of a Two-Tier Unmanned Air System for the MoD Grand Challenge. Presented at Proc. 24th International Conference on Unmanned Air Vehicle Systems
• Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis
  Katramados, I., Crumpler, S., & Breckon, T. (2009, December). Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis. Presented at Proc. International Conference on Computer Vision Systems
• Volumetric Representation for Interactive Video Editing
  Golebiowski, R., Breckon, T., & Flitton, G. (2009, November). Volumetric Representation for Interactive Video Editing. Presented at Proc. 6th European Conference on Visual Media Production
• 3D Colour Mesh Detail Enhancement Driven from 2D Texture Edge Information
  Desile, Q., & Breckon, T. (2008, November). 3D Colour Mesh Detail Enhancement Driven from 2D Texture Edge Information. Presented at Proc. 5th European Conference on Visual Media Production
• Augmenting GPS Speed Limit Monitoring with Road Side Visual Information
  Eichner, M., & Breckon, T. (2008, May). Augmenting GPS Speed Limit Monitoring with Road Side Visual Information. Presented at Proc. IET/ITS Conference on Road Transport Information and Control
• Gesture Recognition using a Laser Pointer
  Rzeznik, J., Barnes, S., & Breckon, T. (2008, November). Gesture Recognition using a Laser Pointer. Presented at Proc. 5th European Conference on Visual Media Production
• Integrated Speed Limit Detection and Recognition from Real-Time Video
  Eichner, M. L., & Breckon, T. (2008, June). Integrated Speed Limit Detection and Recognition from Real-Time Video. Presented at Proc. IEEE Intelligent Vehicles Symposium
• Radicular Cysts and Odontogenic Keratocysts Epithelia Classification using Cascaded Haar Classifiers
  Han, J., Breckon, T., Randell, D., & Landini, G. (2008, July). Radicular Cysts and Odontogenic Keratocysts Epithelia Classification using Cascaded Haar Classifiers. Presented at Proc. 12th Annual Conference on Medical Image Understanding and Analysis
• 3D Measurement for Asset and Environment Authentication and Analysis
  Breckon, T. (2007, June). 3D Measurement for Asset and Environment Authentication and Analysis. Presented at Proc. 4th International Conference on Condition Monitoring
• Considering Video as a Volume
  Flitton, G., & Breckon, T. (2007, November). Considering Video as a Volume. Presented at Proc. 4th European Conference on Visual Media Production
• Real-Time Video Analysis for Vehicle Lights Detection using Temporal Information
  Eichner, M. L., & Breckon, T. (2007, November). Real-Time Video Analysis for Vehicle Lights Detection using Temporal Information. Presented at Proc. 4th European Conference on Visual Media Production
• Combining Motion Segmentation and Feature Based Tracking for Object Classification and Anomaly Detection
  Li, X., & Breckon, T. (2007, November). Combining Motion Segmentation and Feature Based Tracking for Object Classification and Anomaly Detection. Presented at Proc. 4th European Conference on Visual Media Production
• Artwork Image Retrieval using Weighted Colour and Texture Similarity
  Zirnhelt, S., & Breckon, T. (2007, November). Artwork Image Retrieval using Weighted Colour and Texture Similarity. Presented at Proc. 4th European Conference on Visual Media Production
• Direct Geometric Texture Synthesis and Transfer on 3D Meshes
  Breckon, T., & Fisher, R. (2006, November). Direct Geometric Texture Synthesis and Transfer on 3D Meshes. Presented at Proc. 3rd European Conference on Visual Media Production
• Plausible 3D Colour Surface Completion using Non-parametric Techniques
  Breckon, T., & Fisher, R. (2005, September). Plausible 3D Colour Surface Completion using Non-parametric Techniques. Presented at Proc. Mathematics of Surfaces XI Institute of Mathematics and its Applications
• Non-parametric 3D Surface Completion
  Breckon, T., & Fisher, R. (2005, June). Non-parametric 3D Surface Completion. Presented at Proc. Fifth International Conference on 3D Digital Imaging and Modeling
• A Non-parametric Approach to Realistic Surface Completion in 3D Environments
  Breckon, T., & Fisher, R. (2005, April). A Non-parametric Approach to Realistic Surface Completion in 3D Environments. Presented at Proc. Postgraduate Research Conference in Electronics, Photonics, Communications and Networks, and Computing Science
• Environment Authentication through 3D Structural Analysis
  Breckon, T., & Fisher, R. (2004, October). Environment Authentication through 3D Structural Analysis. Presented at Proc. International Conference on Image Analysis and Recognition

Conference Proceeding

• 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
  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

Doctoral Thesis

• Completing Unknown Portions of 3D Scenes via 3D Visual Propogation
  Breckon, T. Completing Unknown Portions of 3D Scenes via 3D Visual Propogation. (Thesis). School of Informatics, University of Edinburgh. https://durham-repository.worktribe.com/output/1642143

Journal Article

• DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
  Poyser, M., & Breckon, T. P. (2026). DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification. Pattern Recognition, 169, Article 111948. https://doi.org/10.1016/j.patcog.2025.111948
• Racial Bias within Face Recognition: A Survey
  Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. P. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, 57(4), 1-39. https://doi.org/10.1145/3705295
• Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation
  Wang, Q., Meng, F., & Breckon, T. P. (2024). Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation. IEEE Transactions on Artificial Intelligence, 5(9), 4403-4414. https://doi.org/10.1109/TAI.2024.3379940
• Neural architecture search: A contemporary literature review for computer vision applications
  Poyser, M., & Breckon, T. P. (2024). Neural architecture search: A contemporary literature review for computer vision applications. Pattern Recognition, 147, 110052. https://doi.org/10.1016/j.patcog.2023.110052
• Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders
  Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033
• Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation
  Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006
• Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
  Wang, Q., & Breckon, T. (2022). Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss. IEEE Transactions on Intelligent Transportation Systems, 23(9), 15233-15243. https://doi.org/10.1109/tits.2021.3138896
• Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video
  Gökstorp, S., & Breckon, T. (2022). Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video. Visual Computer, 38(6), 2033-2040. https://doi.org/10.1007/s00371-021-02264-6
• Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
  Wang, Q., & Breckon, T. (2022). Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation. Pattern Recognition, 123, Article 108362. https://doi.org/10.1016/j.patcog.2021.108362
• Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
  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
• Learning to Drive: End-to-End Off-Road Path Prediction
  Holder, C., & Breckon, T. (2021). Learning to Drive: End-to-End Off-Road Path Prediction. IEEE Intelligent Transportation Systems Magazine, 13(2), 217-221. https://doi.org/10.1109/mits.2019.2898970
• An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
  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
• A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes
  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
• Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
  Maciel-Pearson, B., Akcay, S., Atapour-Abarghouei, A., Holder, C., & Breckon, T. (2019). Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments. IEEE Robotics and Automation Letters, 4(4), 4116-4123. https://doi.org/10.1109/lra.2019.2930496
• Discrete Curvature Representations for Noise Robust Image Corner Detection
  Zhang, W., Sun, C., Breckon, T., & Alshammari, N. (2019). Discrete Curvature Representations for Noise Robust Image Corner Detection. IEEE Transactions on Image Processing, 28(9), 4444-4459. https://doi.org/10.1109/tip.2019.2910655
• Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer
  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
• On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications
  Podmore, J., Breckon, T., Aznan, N., & Connolly, J. (2019). On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 611-618. https://doi.org/10.1109/tnsre.2019.2904791
• On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery
  Mouton, A., & Breckon, T. (2019). On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 27(1), 51-72. https://doi.org/10.3233/xst-180411
• Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery
  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
• Clustering in pursuit of temporal correlation for human motion segmentation
  Qian, C., Breckon, T., & Xu, Z. (2018). Clustering in pursuit of temporal correlation for human motion segmentation. Multimedia Tools and Applications, 77(15), 19615-19631. https://doi.org/10.1007/s11042-017-5408-0
• A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion
  Atapour-Abarghouei, A., & Breckon, T. (2018). A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion. Computers and Graphics, 72, 39-58. https://doi.org/10.1016/j.cag.2018.02.001
• Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels
  Zhang, W., Zhao, Y., Breckon, T., & Chen, L. (2016). Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels. Pattern Recognition, 63(8), 193-205. https://doi.org/10.1016/j.patcog.2016.10.008
• Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications
  Kriechbaumer, T., Blackburn, K., Breckon, T., Hamilton, O., & Riva-Casado, M. (2015). Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications. Sensors, 15(12), 31869-31887. https://doi.org/10.3390/s151229892
• A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening
  Mouton, A., & Breckon, T. (2015). A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 23(5), 531-555. https://doi.org/10.3233/xst-150508
• Robust visual tracking via speedup multiple kernel ridge regression
  Qian, C., Breckon, T. P., & Li, H. (2015). Robust visual tracking via speedup multiple kernel ridge regression. Journal of Electronic Imaging, 24(5), Article 053016. https://doi.org/10.1117/1.jei.24.5.053016
• Real-time construction and visualisation of drift-free video mosaics from unconstrained camera motion
  Breszcz, M., & Breckon, T. (2015). Real-time construction and visualisation of drift-free video mosaics from unconstrained camera motion. Journal of Engineering, 2015(8), 229-240. https://doi.org/10.1049/joe.2015.0016
• Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks
  Flitton, G., Mouton, A., & Breckon, T. (2015). Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks. Pattern Recognition, 48(8), 2489-2499. https://doi.org/10.1016/j.patcog.2015.02.006
• Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening
  Mouton, A., & Breckon, T. (2015). Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening. Pattern Recognition, 48(6), 1961-1978. https://doi.org/10.1016/j.patcog.2015.01.010
• Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery
  Chermak, L., Breckon, T., Flitton, G., & Megherbi, N. (2015). Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery. The Imaging Science Journal, 63(8), 447-457. https://doi.org/10.1179/1743131x15y.0000000019
• A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery
  Flitton, G., Breckon, T., & Megherbi, N. (2013). A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery. Pattern Recognition, 46(9), 2420-2436. https://doi.org/10.1016/j.patcog.2013.02.008
• An Experimental Survey of Metal Artefact Reduction in Computed Tomography
  Mouton, A., Megherbi, N., Van Slambrouck, K., Nuyts, J., & Breckon, T. (2013). An Experimental Survey of Metal Artefact Reduction in Computed Tomography. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 21(2), 193-226. https://doi.org/10.3233/xst-130372
• Cross-Spectral Visual Simultaneous Localization And Mapping (SLAM) with Sensor Handover
  Magnabosco, M., & Breckon, T. (2013). Cross-Spectral Visual Simultaneous Localization And Mapping (SLAM) with Sensor Handover. Robotics and Autonomous Systems, 63(2), 195-208. https://doi.org/10.1016/j.robot.2012.09.023
• An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment
  Mroz, F., & Breckon, T. (2012). An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment. EURASIP Journal on Image and Video Processing, 2012, Article 13. https://doi.org/10.1186/1687-5281-2012-13
• Automatic Real-time Road Marking Recognition Using a Feature Driven Approach
  Kheyrollahi, A., & Breckon, T. (2012). Automatic Real-time Road Marking Recognition Using a Feature Driven Approach. Machine Vision and Applications, 23(1), 123-133. https://doi.org/10.1007/s00138-010-0289-5
• A hierarchical extension to 3D non-parametric surface relief completion
  Breckon, T., & Fisher, R. (2012). A hierarchical extension to 3D non-parametric surface relief completion. Pattern Recognition, 45(1), 172-185. https://doi.org/10.1016/j.patcog.2011.04.021
• The Application of Support Vector Machine Classification to Detect Cell Nuclei for Automated Microscopy
  Han, J., Breckon, T., Randell, D., & Landini, G. (2012). The Application of Support Vector Machine Classification to Detect Cell Nuclei for Automated Microscopy. Machine Vision and Applications, 23(1), 15-24. https://doi.org/10.1007/s00138-010-0275-y
• Automatic Road Environment Classification
  Tang, I., & Breckon, T. (2011). Automatic Road Environment Classification. IEEE Transactions on Intelligent Transportation Systems, 12(2), 476-484. https://doi.org/10.1109/tits.2010.2095499
• Realizing Perceptive Virtual Reality Imaging Applications on Conventional PC Hardware
  Breckon, T., Jenkins, K., & Sonkoly, P. (2011). Realizing Perceptive Virtual Reality Imaging Applications on Conventional PC Hardware. The Imaging Science Journal, 59(1), 1-7. https://doi.org/10.1179/136821910x12750339175907
• Morphologic Characterization of Cell Neighborhoods in Neoplastic and Preneoplastic Epithelium
  Landini, G., Randell, D., Breckon, T., & Han, J. (2010). Morphologic Characterization of Cell Neighborhoods in Neoplastic and Preneoplastic Epithelium. Analytical and quantitative cytology and histology, 32(1), 30-38
• Three-Dimensional Surface Relief Completion Via Nonparametric Techniques
  Breckon, T., & Fisher, R. (2008). Three-Dimensional Surface Relief Completion Via Nonparametric Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2249-2255. https://doi.org/10.1109/tpami.2008.153
• Amodal Volume Completion: 3D Visual Completion
  Breckon, T., & Fisher, R. (2005). Amodal Volume Completion: 3D Visual Completion. Computer Vision and Image Understanding, 99(3), 499-526. https://doi.org/10.1016/j.cviu.2005.05.002