Staff profile

Toby Breckon is a Professor in the Department of Engineering and Department of Computer Science 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 machine learning and pattern recognition techniques, in addition to research-led teaching within the undergraduate Engineering and Computer Science programmes.

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 robotic sensing.

Within the automotive sector, his team work with a number of major vehicle manufacturers on future automotive sensing solutions having originally commenced work in this area in the early days of intelligent driver assistance systems (2007-2023+). 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 (2020).

The work of his team on anomaly detection is used by COSMONiO in their NOUS product. COSMONiO, founded by former members of his research team in 2012, was acquired by Intel in 2020.

As of 2014, his team were selected as a research partner in the UK SAPIENT programme, supplying a fully operational research demonstrator, 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- 2023), 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.

• autonomous sensing
• computer vision
• image processing
• machine learning
• robotic sensing

Authored book

• 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
• Solomon, C., & Breckon, T. (2013). Fundamentos de Processamento Digital de Imagens - Uma Abordagem Pratica com Exemplos em Matlab. LTC
• 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

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

Conference Paper

• Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (in press). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. In ICCV '23: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision
• Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (in press). U3DS3 : Unsupervised 3D Semantic Scene Segmentation.
• Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00301
• Yu, Z., Haung, S., Fang, C., Breckon, T., & Wang, J. (2023). ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.01245
• Li, L., Shum, H. P., & Breckon, T. P. (2023). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.00903
• Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00059
• Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. . https://doi.org/10.5220/0011684700003417
• Wang, Q., Meng, F., & Breckon, T. (2023). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. . https://doi.org/10.1109/IJCNN54540.2023.10191262
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.
• Bhowmik, N., & Breckon, T. (2022). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery.
• Alsehaim, A., & Breckon, T. (2022). VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification.
• Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T., & Willcocks, C. (2022). Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. In ECCV 2022: Computer Vision – ECCV 2022 (170-188)
• Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022). Does lossy image compression affect racial bias within face recognition?.
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2022). Multi-view Vision Transformers for Object Detection.
• Groom, M., & Breckon, T. (2022). On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision.
• Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.
• Bhowmik, N., Barker, J., Gaus, Y., & Breckon, T. (2022). Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery. . https://doi.org/10.1109/cvprw56347.2022.00052
• Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. . https://doi.org/10.1109/cvprw56347.2022.00048
• Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. (2022). Measuring Hidden Bias within Face Recognition via Racial Phenotypes. . https://doi.org/10.1109/wacv51458.2022.00326
• Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. (2022). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. . https://doi.org/10.5220/0010836600003124
• Raju, J., Gaus, Y., & Breckon, T. (2021). Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention. . https://doi.org/10.1109/icmla52953.2021.00115
• Wang, Q., & Breckon, T. (2021). Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. . https://doi.org/10.1109/icmla52953.2021.00020
• Webb, T., Bhowmik, N., Gaus, Y., & Breckon, T. (2021). Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery. . https://doi.org/10.1109/icmla52953.2021.00102
• Li, L., Ismail, K. N., Shum, H. P., & Breckon, T. P. (2021). DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. . https://doi.org/10.1109/3dv53792.2021.00130
• Alsehaim, A., & Breckon, T. (2021). Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition.
• Bhowmik, N., Gaus, Y., & Breckon, T. (2021). On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.
• Wang, Q., & Breckon, T. (2021). Source Class Selection with Label Propagation for Partial Domain Adaptation.
• Alshammari, N., Akcay, S., & Breckon, T. (2021). Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation.
• Alshammari, N., Akcay, S., & Breckon, T. (2021). Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation.
• Thomson, W., Bhowmik, N., & Breckon, T. (2021). Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. . https://doi.org/10.1109/icmla51294.2020.00030
• Barker, J., & Breckon, T. (2021). PANDA: Perceptually Aware Neural Detection of Anomalies. . https://doi.org/10.1109/ijcnn52387.2021.9534399
• Sasaki, H., Willcocks, C., & Breckon, T. (2021). Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery. . https://doi.org/10.1109/icpr48806.2021.9413023
• Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. . https://doi.org/10.1109/iccvw54120.2021.00142
• Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., & Breckon, T. (2021). Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI. . https://doi.org/10.1109/icpr48806.2021.9411994
• Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021). Autoencoders Without Reconstruction for Textural Anomaly Detection. . https://doi.org/10.1109/ijcnn52387.2021.9533804
• Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197
• Wang, Q., & Breckon, T. (2021). On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening. In 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. https://doi.org/10.1109/ijcnn52387.2021.9533631
• Wang, Q., Bhowmik, N., & Breckon, T. (2021). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. . https://doi.org/10.1109/icmla51294.2020.00012
• Poyser, M., Atapour-Abarghouei, A., & Breckon, T. (2021). On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. . https://doi.org/10.1109/icpr48806.2021.9412455
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2021). Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery. . https://doi.org/10.1109/icpr48806.2021.9413007
• Wang, Q., Bhowmik, N., & Breckon, T. (2020). On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN) (1-8). https://doi.org/10.1109/ijcnn48605.2020.9207389
• Gaus, Y., Bhowmik, N., Isaac-Medina, B., & Breckon, T. (2020). Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery. In H. Bouma, R. Prabhu, R. J. Stokes, & Y. Yitzhaky (Eds.), Proceedings volume 11542, counterterrorism, crime fighting, forensics, and surveillance technologies IV. https://doi.org/10.1117/12.2573968
• Alsehaim, A., & Breckon, T. (2020). Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification.
• Yucer, S., Akcay, S., Al Moubayed, N., & Breckon, T. (2020). Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation.
• Wang, Q., & Breckon, T. (2020). Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling. In AAAI-20 / IAAI-20 / EAAI-20 proceedings (6243-6250). https://doi.org/10.1609/aaai.v34i04.6091
• Samarth, G., Bhowmik, N., & Breckon, T. (2019). Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. In M. . A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. (. Seliya (Eds.), Proceedings of the 18th IEEE International Conference on Machine Learning and Applications ICMLA 2019 (653-658). https://doi.org/10.1109/icmla.2019.00119
• Gaus, Y., Bhowmik, N., & Breckon, T. (2019). On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery. In Proceeding of the International Symposium on Technologies for Homeland Security (1-7). https://doi.org/10.1109/hst47167.2019.9032917
• Akcay, A., Atapour-Abarghouei, A., & Breckon, T. P. (2019). Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2019.8851808
• Bhowmik, N., Wang, Q., Gaus, Y., Szarek, M., & Breckon, T. (2019). The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery.
• Atapour-Abarghouei, A., & Breckon, T. (2019). Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (4295-4299). https://doi.org/10.1109/icip.2019.8803551
• Peng, S., Kamata, S., & Breckon, T. (2019). A Ranking based Attention Approach for Visual Tracking. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (3073-3077). https://doi.org/10.1109/icip.2019.8803358
• Atapour-Abarghouei, A., & Breckon, T. P. (2019). To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation. In Proceedings of 2019 International Conference on 3D Vision (3DV) (183-193). https://doi.org/10.1109/3dv.2019.00029
• Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. In 2019 International Conference on Robotics and Automation (ICRA) ; proceedings (4889-4895). https://doi.org/10.1109/icra.2019.8794060
• Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. In 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings (1-8). https://doi.org/10.1109/ijcnn.2019.8852227
• Atapour-Abarghouei, A., & Breckon, T. (2019). Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. In IEEE Conference on Computer Vision and Pattern Recognition, Deep Vision Long Beach, CA, USA, 16-20 June 2019
• Bhowmik, N., Gaus, Y., & Breckon, T. (2019). Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items. In Proceeding of the International Symposium on Technologies for Homeland Security (1-6). https://doi.org/10.1109/hst47167.2019.9032920
• Gaus, Y., Bhowmik, N., Akcay, S., & Breckon, T. (2019). Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (420-425). https://doi.org/10.1109/icmla.2019.00079
• Adey, P., Bordewich, M., Breckon, T., & Hamilton, O. (2019). Region Based Anomaly Detection With Real-Time Training and Analysis. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (495-499). https://doi.org/10.1109/icmla.2019.00092
• Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019). Style Augmentation: Data Augmentation via Style Randomization.
• Ismail, K., & Breckon, T. (2019). On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (641-646). https://doi.org/10.1109/icmla.2019.00117
• Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. In C. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision – ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III (622-637). https://doi.org/10.1007/978-3-030-20893-6_39
• Gaus, Y., Bhowmik, N., Akcay, A., Guillen-Garcia, P., Barker, J., & Breckon, T. (2019). Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery. In 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings. https://doi.org/10.1109/ijcnn.2019.8851829
• Bhowmik, N., Gaus, Y., Akcay, S., Barker, J., & Breckon, T. (2019). On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (986-991). https://doi.org/10.1109/icmla.2019.00168
• Wang, Q., Bu, P., & Breckon, T. (2019). Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition. In 2019 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2019.8852015
• Stephenson, F., Breckon, T., & Katramados, I. (2019). DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (1277-1281). https://doi.org/10.1109/icip.2019.8803739
• Wang, Q., Ning, J., & Breckon, T. (2019). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (644-648). https://doi.org/10.1109/icip.2019.8803793
• Guo, T., Akcay, S., Adey, P., & Breckon, T. (2018). On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks.
• Dong, Z., Kamata, S., & Breckon, T. (2018). Infrared Image Colorization Using S-Shape Network. In 2018 25th IEEE International Conference on Image Processing (ICIP) : October 7–10, 2018, Megaron Athens International Conference Centre, Athens, Greece. Proceedings (2242-2246). https://doi.org/10.1109/icip.2018.8451230
• Dunnings, A., & Breckon, T. (2018). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. In 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, October 7-10, 2018. Proceedings (1358-1362). https://doi.org/10.1109/icip.2018.8451657
• Payen de La Garanderie, G., Atapour-Abarghouei, A., & Breckon, T. (2018). Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery.
• Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. In Proceedings of the 19th Towards Autonomous Robotic Systems (TAROS) Conference : Bristol, England, 25-27 July 2018 (1-11)
• Holder, C., & Breckon, T. (2018). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction.
• Lin, K., & Breckon, T. (2018). Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras. . https://doi.org/10.1007/978-3-319-93000-8_36
• Holder, C., & Breckon, T. (2018). Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments. . https://doi.org/10.1007/978-3-319-93000-8_48
• Loveday, M., & Breckon, T. (2018). On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling.
• Atapour-Abarghouei, A., & Breckon, T. P. (2018). Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion. In A. Campilho, F. Karray, & B. T. H. Romeny (Eds.), Image analysis and recognition : 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018 ; proceedings (306-314). https://doi.org/10.1007/978-3-319-93000-8_35
• Alshammari, N., Akcay, S., & Breckon, T. (2018). On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding.
• Atapour-Abarghouei, A., & Breckon, T. (2018). Extended Patch Prioritization For Depth Hole Filling Within Constrained Exemplar-Based RGB-D Image Completion.
• Atapour-Abarghouei, A., & Breckon, T. (2018). Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), 18-22 June 2018, Salt Lake City, Utah (2800-2810). https://doi.org/10.1109/cvpr.2018.00296
• Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631
• Maciel-Pearson, B., & Breckon, T. (2017). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. In UK-RAS Conference: 'Robots working for & among us' proceedings (19-21)
• Wu, R., Kamata, S., & Breckon, T. (2017). Face Recognition via Deep Sparse Graph Neural Networks.
• Atapour-Abarghouei, A., & Breckon, T. (2017). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation.
• Kundegorski, M., Akcay, S., Payen de La Garanderie, G., Breckon, T., & Stokes, R. (2016). Real-time Classification of Vehicle Types within Infra-red Imagery. In D. Burgess, F. Carlysle-Davies, G. Owen, H. Bouma, R. Stokes, & Y. Yitzhaky (Eds.), Optics and photonics for counterterrorism, crime fighting, and defence XII. https://doi.org/10.1117/12.2241106
• Holder, C., Breckon, T., & Wei, X. (2016). From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes. In G. Hua, & H. Jégou (Eds.), Computer Vision – ECCV 2016 workshops : Amsterdam, The Netherlands, October 8-10 and 15-16, 2016. Proceedings. Part I (149-162). https://doi.org/10.1007/978-3-319-46604-0_11
• Akcay, S., Kundegorski, M., Devereux, M., & Breckon, T. (2016). Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (1057-1061). https://doi.org/10.1109/icip.2016.7532519
• Sugimoto, K., Breckon, T., & Kamata, S. (2016). Constant-time Bilateral Filter using Spectral Decomposition. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (3319-3323). https://doi.org/10.1109/icip.2016.7532974
• Katramados, I., & Breckon, T. (2016). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (300-304). https://doi.org/10.1109/icip.2016.7532367
• Hamilton, O., & Breckon, T. (2016). Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (3439-3443). https://doi.org/10.1109/icip.2016.7532998
• Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II (423-430). https://doi.org/10.1007/978-3-319-44781-0_50
• Thomas, P., Marshall, G., Faulkner, D., Kent, P., Page, S., Islip, S., …Styles, T. (2016). Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR). In M. A. Kolodny, & T. Pham (Eds.), Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII (1-18). https://doi.org/10.1117/12.2229720
• Atapour-Abarghouei, A., de La Garanderie, G. P., & Breckon, T. P. (2016). Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery. . https://doi.org/10.1109/icpr.2016.7900062
• Kundegorski, M., Akcay, S., Devereux, M., Mouton, A., & Breckon, T. (2016). On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening. In 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) ; proceedings. https://doi.org/10.1049/ic.2016.0080
• Webster, D., & Breckon, T. (2015). Improved raindrop detection using combined shape and saliency descriptors with scene context isolation. In 2015 IEEE International Conference on Image Processing, ICIP 2015, 27-30 September 2015, Quebec City, QC, Canada ; proceedings (4376-4380). https://doi.org/10.1109/icip.2015.7351633
• Cavestany, P., Rodríguez, A., Martínez-Barberá, H., & Breckon, T. (2015). Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots. In 2015 IEEE International Conference on Image Processing, ICIP 2015, 27-30 September 2015, Quebec City, QC, Canada ; proceedings (4927-4931). https://doi.org/10.1109/icip.2015.7351744
• Kundegorski, M., & Breckon, T. (2015). Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery. . https://doi.org/10.1117/12.2195050
• Kurcius, J., & Breckon, T. (2014). Using Compressed Audio-visual Words for Multi-modal Scene Classification. In Computational Intelligence for Multimedia Understanding (IWCIM), 2014 International Workshop on, 1-2 November 2014, Paris, France ; proceedings (99-103). https://doi.org/10.1109/iwcim.2014.7008808
• Walger, D., Breckon, T., Gaszczak, A., & Popham, T. (2014). A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions. In Computational Intelligence for Multimedia Understanding (IWCIM), 2014 International Workshop on, 1-2 November 2014, Paris, France ; proceedings (84-89). https://doi.org/10.1109/iwcim.2014.7008805
• Mouton, A., Breckon, T., Flitton, G., & Megherbi, N. (2014). 3D object classification in baggage computed tomography imagery using randomised clustering forests. In Image Processing (ICIP), 2014 IEEE International Conference on, 27-30 October 2014, Paris, France ; proceedings (5202-5206). https://doi.org/10.1109/icip.2014.7026053
• Kundegorski, M., & Breckon, T. (2014). A photogrammetric approach for real-time 3D localization and tracking of pedestrians in monocular infrared imagery. In Optics and photonics for counterterrorism, crime fighting, and defence X ; and optical materials and biomaterials in security and defence systems technology XI. https://doi.org/10.1117/12.2065673
• Payen de La Garanderie, G., & Breckon, T. (2014). Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo. In M. Valstar, A. French, & T. Pridmore (Eds.), Proceedings of the British Machine Vision Conference (417.1-417.12). https://doi.org/10.5244/C.28.110
• Megherbi, N., Breckon, T., & Flitton, G. (2013). Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection. . https://doi.org/10.1117/12.2028509
• Megherbi, N., Breckon, T., Flitton, G., & Mouton, A. (2013). Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection. . https://doi.org/10.1117/12.2028506
• Han, J., Gaszczak, A., Maciol, R., Barnes, S., & Breckon, T. (2013). Human Pose Classification within the Context of Near-IR Imagery Tracking. . https://doi.org/10.1117/12.2028375
• Mise, O., & Breckon, T. (2013). Image Super-Resolution applied to moving targets in high dynamics scenes. . https://doi.org/10.1117/12.2028743
• Hamilton, O., Breckon, T., Bai, X., & Kamata, S. (2013). A Foreground Object based Quantitative Assessment of Dense Stereo Approaches for use in Automotive Environments. . https://doi.org/10.1109/icip.2013.6738086
• Mouton, A., Megherbi, N., Breckon, T., Van Slambrouck, K., & Nuyts, J. (2013). A Distance Weighted Method for Metal Artefact Reduction in CT. . https://doi.org/10.1109/icip.2013.6738481
• Breckon, T., Gaszczak, A., Han, J., Eichner, M., & Barnes, S. (2013). Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance. . https://doi.org/10.1117/12.2028340
• Chereau, R., & Breckon, T. (2013). Robust Motion Filtering as an Enabler to Video Stabilization for a Tele-operated Mobile Robot. . https://doi.org/10.1117/12.2028360
• Faria, J., Bagley, S., Rueger, S., & Breckon, T. (2013). Challenges of Finding Aesthetically Pleasing Images. . https://doi.org/10.1109/wiamis.2013.6616162
• Turcsany, D., Mouton, A., & Breckon, T. (2013). Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words. . https://doi.org/10.1109/icit.2013.6505833
• Mioulet, L., Breckon, T., Mouton, A., Liang, H., & Morie, T. (2013). Gabor Features for Real-Time Road Environment Classification. . https://doi.org/10.1109/icit.2013.6505829
• Breckon, T., Han, J., & Richardson, J. (2012). Consistency in Muti-modal Automated Target Detection using Temporally Filtered Reporting. . https://doi.org/10.1117/12.974559
• Megherbi, N., Breckon, T., Flitton, G., & Mouton, A. (2012). Fully Automatic 3D Threat Image Projection: Application to Densely Cluttered 3D Computed Tomography Baggage Images. . https://doi.org/10.1109/ipta.2012.6469523
• Mouton, A., Megherbi, N., Flitton, G., Bizot, S., & Breckon, T. (2012). A Novel Intensity Limiting Approach to Metal Artefact Reduction in 3D CT Baggage Imagery. . https://doi.org/10.1109/icip.2012.6467295
• Megherbi, N., Han, J., Flitton, G., & Breckon, T. (2012). A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening. . https://doi.org/10.1109/icip.2012.6467558
• Pinggera, P., Breckon, T., & Bischof, H. (2012). On Cross-Spectral Stereo Matching using Dense Gradient Features. . https://doi.org/10.5244/c.26.103
• Carey, D., Shepherd, N., Kendall, C., Stone, N., Breckon, T., & Lloyd, G. (2012). Correlating Histology and Spectroscopy to Differentiate Pathologies of the Colon.
• Flitton, G., Breckon, T., & Megherbi, N. (2012). A 3D extension to cortex like mechanisms for 3D object class recognition. . https://doi.org/10.1109/cvpr.2012.6248109
• Bordes, L., Breckon, T., Katramados, I., & Kheyrollahi, A. (2011). Adaptive Object Placement for Augmented Reality Use in Driver Assistance Systems.
• Heras, A., Breckon, T., & Tirovic, M. (2011). Video Re-sampling and Content Re-targeting for Realistic Driving Incident Simulation.
• Katramados, I., & Breckon, T. (2011). Real-time Visual Saliency by Division of Gaussians. . https://doi.org/10.1109/icip.2011.6115785
• Chenebert, A., Breckon, T., & Gaszczak, A. (2011). A Non-temporal Texture Driven Approach to Real-time Fire Detection. . https://doi.org/10.1109/icip.2011.6115796
• Breszcz, M., Breckon, T., & Cowling, I. (2011). Real-time Mosaicing from Unconstrained Video Imagery for UAV Applications.
• Gaszczak, A., Breckon, T., & Han, J. (2011). Real-time People and Vehicle Detection from UAV Imagery. . https://doi.org/10.1117/12.876663
• Megherbi, N., Flitton, G., & Breckon, T. (2010). A Classifier based Approach for the Detection of Potential Threats in CT based Baggage Screening. . https://doi.org/10.1109/icip.2010.5653676
• Flitton, G., Breckon, T., & Megherbi, N. (2010). Object Recognition using 3D SIFT in Complex CT Volumes. . https://doi.org/10.5244/c.24.11
• Kowaliszyn, M., & Breckon, T. (2010). Automatic Road Feature Detection and Correlation for the Correction of Consumer Satellite Navigation System Mapping. . https://doi.org/10.1049/cp.2010.0397
• Sokalski, J., Breckon, T., & Cowling, I. (2010). Automatic Salient Object Detection in UAV Imagery.
• Golebiowski, R., Breckon, T., & Flitton, G. (2009). Volumetric Representation for Interactive Video Editing.
• Breckon, T., Barnes, S., Eichner, M., & Wahren, K. (2009). Autonomous Real-time Vehicle Detection from a Medium-Level UAV.
• Wahren, K., Cowling, I., Patel, Y., Smith, P., & Breckon, T. (2009). Development of a Two-Tier Unmanned Air System for the MoD Grand Challenge.
• Katramados, I., Crumpler, S., & Breckon, T. (2009). Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis. . https://doi.org/10.1007/978-3-642-04667-4_27
• Desile, Q., & Breckon, T. (2008). 3D Colour Mesh Detail Enhancement Driven from 2D Texture Edge Information. . https://doi.org/10.1049/cp%3A20081087
• Rzeznik, J., Barnes, S., & Breckon, T. (2008). Gesture Recognition using a Laser Pointer. . https://doi.org/10.1049/cp%3A20081085
• Han, J., Breckon, T., Randell, D., & Landini, G. (2008). Radicular cysts and odontogenic keratocysts epithelia classification using cascaded Haar classifiers.
• Eichner, M., & Breckon, T. (2008). Integrated Speed Limit Detection and Recognition from Real-Time Video. . https://doi.org/10.1109/ivs.2008.4621285
• Eichner, M., & Breckon, T. (2008). Augmenting GPS Speed Limit Monitoring with Road Side Visual Information.
• Flitton, G., & Breckon, T. (2007). Considering Video as a Volume.
• Li, X., & Breckon, T. (2007). Combining Motion Segmentation and Feature Based Tracking for Object Classification and Anomaly Detection.
• Zirnhelt, S., & Breckon, T. (2007). Artwork Image Retrieval using Weighted Colour and Texture Similarity.
• Eichner, M., & Breckon, T. (2007). Real-Time Video Analysis for Vehicle Lights Detection using Temporal Information.
• Breckon, T. (2007). 3D Measurement for Asset and Environment Authentication and Analysis.
• Breckon, T., & Fisher, R. (2006). Direct Geometric Texture Synthesis and Transfer on 3D Meshes.
• Breckon, T., & Fisher, R. (2005). Plausible 3D Colour Surface Completion using Non-parametric Techniques. . https://doi.org/10.1007/11537908_7
• Breckon, T., & Fisher, R. (2005). Non-parametric 3D Surface Completion. . https://doi.org/10.1109/3dim.2005.61
• Breckon, T., & Fisher, R. (2005). A Non-parametric Approach to Realistic Surface Completion in 3D Environments.
• Breckon, T., & Fisher, R. (2004). Environment Authentication through 3D Structural Analysis. . https://doi.org/10.1007/978-3-540-30125-7_84

Doctoral Thesis

• Breckon, T. (2006). Completing Unknown Portions of 3D Scenes via 3D Visual Propogation. (Thesis). School of Informatics, University of Edinburgh. Retrieved from https://durham-repository.worktribe.com/output/1642143

Journal Article

• 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
• 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
• 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
• 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
• 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
• 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
• Wang, Q., & Breckon, T. (2022). Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/tits.2021.3138896
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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, https://doi.org/10.1179/1743131x15y.0000000019
• 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
• 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
• 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
• 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
• 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
• 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
• Mouton, A., Megherbi, N., Van Slambrouck, K., Nuyts, J., & Breckon, T. (2013). An Experimental Survey of Metal Artefact Reduction in Computed Tomography. https://doi.org/10.3233/xst-130372
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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