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 generalized 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 breath 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 (2007-2020+). As of 2019, he is a scientific advisor to Machines With Vision on autonomous vehicle sensing.

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 insertion (TIP) within 3D CT security scan imagery. 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 (as of 2019/20).

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 automated visual surveillance across passive infrared (thermal) imagery resulting in substantial impact into the defence and security sensing sector (2016-2020+).

In 2008 he led the development of image-based automatic threat detection for the 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 QinetiQ as well as holding prior positions with the schools inspectorate OFSTED, the Scottish Language Dictionaries organisation and (1990s, dot-com) software house Orbital Software.

He has held a 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 scientific advisor to H.M. Cabinet Office (Cyber Security Expert Group, 2015-present) and previously to H.M. Government Office for Science (2016/17) in areas pertaining to his research specialism.

At Durham, Prof. Breckon led applied Computer Science research, as Head of Innovative Computing research, between 2014-2018. 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 2010 he has been 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.

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

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

• Innovative Computing
• Sustainable Infrastructure

• Image Processing Textbook
• Personal Home Page

• 2019: KTP Cievert- Innovate UK(£152457.20 from Innovate UK)

Available for media contact about:

• Computer Science: image processing
• Computer Science: object recognition
• Computer Science: computer vision
• Computer Science: robotic sensing
• Computer Science: machine learning

Authored book

• Fisher, R.B., Breckon, T.P., Dawson-Howe, K., Fitzgibbon, A., Robertson, C., Trucco, E. & Williams, C.K.I. (2014). Dictionary of Computer Vision and Image Processing. Wiley.
• Solomon, C.J. & Breckon, T.P. (2013). Fundamentos de Processamento Digital de Imagens - Uma Abordagem Pratica com Exemplos em Matlab. Brazil: LTC.
• Solomon, C.J. & Breckon, T.P. (2010). Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley-Blackwell.

Chapter in book

• Atapour-Abarghouei, A. & Breckon, T.P. (2020). Domain Adaptation via Image Style Transfer. In Domain Adaptation in Computer Vision with Deep Learning. Venkateswara, Hemanth & Panchanathan, Sethuraman Cham: Springer. 137-156.
• Atapour-Abarghouei, A. & Breckon, T.P. (2019). Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation. In RGB-D Image Analysis and Processing. Rosin, Paul L. Lai, Yu-Kun Shao, Ling & Liu, Yonghuai Cham: Springer. 15-50.

Conference Paper

• Wang, Q. & Breckon, T.P. (2021), Source Class Selection with Label Propagation for Partial Domain Adaptation, International Conference on Image Processing. Anchorage, AK, IEEE.
• Isaac-Medina, B.K.S., Willcocks, C.G. & Breckon, T.P. (2021), Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery, 25th International Conference on Pattern Recognition (ICPR 2020). Milan, Italy, IEEE.
• Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J. & Breckon, T.P. (2021), Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI, 25th International Conference on Pattern Recognition (ICPR 2020). Milan, Italy, IEEE.
• Bhowmik, N., Gaus, Y.F.A. & Breckon, T.P. (2021), On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks, International Conference on Image Processing. Anchorage, AK, IEEE.
• Wang, Q. & Breckon, T.P. (2021), On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening, Proc. Int. Joint Conference on Neural Networks. IEEE.
• Prew, W., Breckon, T.P., Bordewich, M.J.R. & Beierholm, U. (2021), Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss, 25th International Conference on Pattern Recognition (ICPR 2020). Milan, Italy, IEEE.
• Alshammari, N., Akcay, S. & Breckon, T.P. (2021), Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation, IEEE Intelligent Transportation Systems Society. IEEE.
• Alshammari, N., Akcay, S. & Breckon, T.P. (2021), Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation, 2021 IEEE Intelligent Vehicles Symposium (IV 2021). Nagoya, Japan, IEEE.
• Sasaki, H., Willcocks, C.G. & Breckon, T.P. (2021), Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery, 25th International Conference on Pattern Recognition (ICPR 2020). Milan, Italy, IEEE.
• Alsehaim, A. & Breckon, T.P. (2020), Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification, 25th International Conference on Pattern Recognition (ICPR2020). Milan, Italy, IEEE.
• Wang, Q., Bhowmik, N. & Breckon, T.P. (2020), Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery, 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020). Miami, Florida, IEEE.
• Yucer, S, Akcay, S, Al Moubayed, N & Breckon, T.P (2020), Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation, Computer Vision and Pattern Recognition Workshops. Seattle, USA, IEEE.
• Wang, Q. & Breckon, T.P. (2020), Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling, 34: Thirty Fourth AAAI Conference on Artificial Intelligence. New York, USA, AAAI Press, Palo Alto, 6243-6250.
• Poyser, M., Atapour-Abarghouei, A. & Breckon, T.P. (2020), On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures, 25th International Conference on Pattern Recognition (ICPR2020). Milan, Italy, IEEE.
• Thompson, W., Bhowmik, N. & Breckon, T.P. (2020), Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection, 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020). Miami, USA, IEEE.
• Wang, Q., Bhowmik, N. & Breckon, T.P. (2020), On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery, International Joint Conference on Neural Networks. Glasgow, Scotland, IEEE, Piscataway, NJ, 1-8.
• Gaus Y.F.A., Bhowmik, N., Isaac-Medina, B.K.S. & Breckon, T.P. (2020), Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery, in Bouma, H., Prabhu, R., Stokes, R.J. & Yitzhaky, Y. eds, 11542: Spie Security + Defence. SPIE, 11540205.
• Bhowmik, N., Gaus, Y.F.A. & Breckon, T.P. (2019), Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items, 2019 IEEE International Symposium on Technologies for Homeland Security. Boston, USA, IEEE, Piscataway, NJ, 1-6.
• Aznan, N.K.N., Connolly, J.D., Al Moubayed, N. & Breckon, T.P. (2019), Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation, 2019 IEEE International Conference on Robotics and Automation (ICRA). Montreal, Canada, IEEE, 4889-4895.
• Atapour-Abarghouei, A. & Breckon, T.P. (2019), Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision. Long Beach, California, USA, IEEE.
• Stephenson, F., Breckon, T.P. & Katramados, I. (2019), DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 1277-1281.
• Wang, Q., Ning, J. & Breckon, T.P. (2019), A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 644-648.
• Akcay, Samet, Atapour-Abarghouei, Amir & Breckon, Toby P. (2019), GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training, in Jawahar, C. V., Li, Hongdong, Mori, Greg & Schindler, Konrad eds, Lecture Notes in Computer Science 11363: 14th Asian Conference on Computer Vision (ACCV). Perth, Australia, Springer, 622-637.
• Gaus, Y.F.A., Bhowmik, N., Akcay, S. & Breckon, T.P. (2019), Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery, 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Boca Raton, Florida, USA, IEEE, Piscataway, NJ, 420-425.
• Adey, P., Bordewich, M., Breckon, T.P. & O.K. Hamilton (2019), Region Based Anomaly Detection With Real-Time Training and Analysis, 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Boca Raton, Florida, USA, IEEE, Piscataway, NJ, 495-499.
• 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, International Conference on 3D Vision. Quebec, IEEE, Piscataway, NJ, 183-193.
• Philip Jackson, Amir Atapour-Abarghouei, Stephen Bonner, Toby Breckon & Boguslaw Obara (2019), Style augmentation: data augmentation via style randomization, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision. Long Beach, CA, USA, IEEE.
• Gaus, Y.F.A., Bhowmik, N. & Breckon, T.P. (2019), On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery, 2019 IEEE International Symposium on Technologies for Homeland Security. Boston, USA, IEEE, Piscataway, NJ, 1-7.
• Samarth, G., Bhowmik, N. & Breckon, T.P. (2019), Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection, in Wani, M. Arif, Khoshgoftaar, Taghi M., Wang, Dingding, Wang, Huanjing & Seliya Naeem (Jim) eds, 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Boca Raton, Florida, USA, IEEE, Piscataway, NJ, 653-658.
• Atapour-Abarghouei, A. & Breckon, T.P. (2019), Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior, IEEE International Conference on Image Processing. Taipei, Taiwan, IEEE, Piscataway, NJ, 4295-4299.
• Bhowmik, N., Wang, Q., Gaus, Y.F.A., Szarek, M. & Breckon, T.P. (2019), The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery, British Machine Vision Conference Workshops. Cardiff, Wales, UK, BMVA, 1-8.
• Aznan, N.K.N., Atapour-Abarghouei, A., Bonner, S., Connolly, J.D., Al Moubayed, N. & Breckon, T.P. (2019), Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification, International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, IEEE, 1-8.
• Peng, S., Kamata, S. & Breckon, T.P. (2019), A Ranking based Attention Approach for Visual Tracking, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 3073-3077.
• Akcay, A., Atapour-Abarghouei, A. & Breckon, T.P. (2019), Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection, Proc. Int. Joint Conference on Neural Networks. Budapest, Hungary, IEEE.
• Gaus, Y.F.A., Bhowmik, N., Akcay, A., Guillen-Garcia, P.M., Barker, J.W & Breckon, T.P. (2019), Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery, Joint Conference on Neural Networks. Budapest, Hungary, IEEE.
• Ismail, K.N. & Breckon, T.P. (2019), On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding, 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Boca Raton, Florida, USA, IEEE, Piscataway, NJ, 641-646.
• Bhowmik, N., Gaus, Y.F.A., Akcay, S., Barker, J.W. & Breckon, T.P. (2019), On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery, 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Boca Raton, Florida, USA, IEEE, Piscataway, NJ, 986-991.
• Wang, Q., Bu, P. & Breckon, T.P. (2019), Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition, International Joint Conference on Neural Networks. Budapest, IEEE.
• Dunnings, A. & Breckon, T.P. (2018), Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection, 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece, IEEE, Piscataway, NJ, 1358-1362.
• Atapour-Abarghouei, Amir & Breckon, Toby P. (2018), Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion, in Campilho, Aurélio, Karray, Fakhri & Romeny, Bart ter Haar eds, Lecture Notes in Computer Science 10882: International Conference Image Analysis and Recognition. Póvoa de Varzim, Portugal, Springer, 306-314.
• Lin, K. & Breckon, T.P. (2018), Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras, 15th International Conference on Image Analysis and Recognition (ICIAR 2018). Póvoa de Varzim, Springer.
• Dong, Z., Kamata, S. & Breckon, T.P. (2018), Infrared Image Colorization Using S-Shape Network, 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece, IEEE, Piscataway, 2242-2246.
• Atapour-Abarghouei, A. & Breckon, T.P. (2018), Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer, 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, Utah, IEEE, Piscataway, NJ, 2800-2810.
• Alshammari, N., Akcay, S. & Breckon, T.P. (2018), On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding, The 29th IEEE Intelligent Vehicles Symposium (IEEE IV 2018). Changshu, China, IEEE.
• Maciel-Pearson, B.G., Carbonneau, P. & Breckon, T.P. (2018), Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy, Lecture Notes in Computer Science 19th Towards Autonomous Robotic Systems (TAROS) Conference. Bristol, Springer, 1-11.
• Holder, C. & Breckon, T.P. (2018), Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction, The 29th Intelligent Vehicles Symposium (IEEE IV 2018). Changshu, China, IEEE.
• Guo, T., Akcay, S., Adey, P. & Breckon, T.P. (2018), On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks, 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece, IEEE.
• Loveday, M. & Breckon, T.P. (2018), On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling, Computer Vision and Pattern Recognition Workshops (CVPR) 2018. Salt Lake City, Utah, IEEE.
• Aznan, N.K.N., Bonner, S., Connolly, J.D., Al Moubayed, N. & Breckon, T.P. (2018), On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018). Miyazaki, Japan, IEEE, Piscataway, NJ, 3726-3731.
• Payen de La Garanderie, G., Atapour-Abarghouei, A. & Breckon, T.P. (2018), Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery, Lecture Notes in Computer Science (LNCS) European Conference on Computer Vision. Munich, Germany, Springer.
• Holder, C.J. & Breckon, T.P. (2018), Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments, 15th International Conference on Image Analysis and Recognition (ICIAR 2018). Póvoa de Varzim, Portugal, Springer.
• Atapour-Abarghouei, A. & Breckon, T.P. (2018), Extended Patch Prioritization For Depth Hole Filling Within Constrained Exemplar-Based RGB-D Image Completion, 15th International Conference on Image Analysis and Recognition (ICIAR 2018). Póvoa de Varzim, Portugal, Springer.
• Wu, R., Kamata, S. & Breckon, T.P. (2017), Face Recognition via Deep Sparse Graph Neural Networks, British Machine Vision Conference Workshops. London, British Machine Vision Association (BMVA).
• Atapour-Abarghouei, A. & Breckon, T.P. (2017), DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation, 28th British Machine Vision Conference (BMVC) 2017. London, British Machine Vision Association (BMVA).
• Maciel-Pearson, B.G. & Breckon, T.P. (2017), An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy, The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us. Bristol, UK Robotics and Autonomous Systems Network, 19-23.
• Thomas, P.A., Marshall, G.F., Faulkner, D., Kent, P., Page, S., Islip, S., Oldfield, J., Breckon, T.P., Kundegorski, M.E., Clarke, D. & Styles, T. (2016), Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR), in Kolodny, Michael A. & Pham, Tien eds, Proceedings of SPIE 9831: SPIE Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent Intelligence Surveillance and Reconnaissance VII. Baltimore, Maryland, SPIE, Bellingham, WA, 983108.
• Al Moubayed, N., Breckon, T.P., Matthews, P.C. & McGough, A.S. (2016), SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder, in Villa, Alessandro E.P., Masulli, Paolo & Pons Rivero, Antonio J. eds, Lecture Notes in Computer Science 9887: Springer International Publishing, Cham, 423-430.
• Akcay, S., Kundegorski, M.E., Devereux, M. & Breckon, T.P. (2016), Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery, 2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA, IEEE, Piscataway, NJ, 1057-1061.
• Holder, C.J., Breckon, T.P. & 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 Hua, Gang & Jégou, Hervé eds, Lecture Notes in Computer Science 9913: European Conference on Computer Vision Workshops. Amsterdam, Springer, Cham, Switzerland, 149-162.
• Sugimoto, K., Breckon, T.P. & Kamata, S. (2016), Constant-time Bilateral Filter using Spectral Decomposition, 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA, IEEE, Piscataway, NJ, 3319-3323.
• Katramados, I. & Breckon, T.P. (2016), Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications, 2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA, IEEE, Piscataway, NJ, 300-304.
• Kundegorski, M.E., Akcay, S., Devereux, M., Mouton, A. & Breckon, T.P. (2016), On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening, International Conference on Imaging for Crime Detection and Prevention. Madrid, Spain, IET, 12 (6).
• Kundegorski, M.E., Akcay, S., Payen de La Garanderie, G. & Breckon, T.P. (2016), Real-time Classification of Vehicle Types within Infra-red Imagery, in Burgess, D., Owen, G., Bouma, H., Carlysle-Davies, F., Stokes, R.J. & Yitzhaky, Y. eds, Proceedings of SPIE 9995: Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII. Edinburgh, United Kingdom, SPIE (Society of Photo-optical Instrumentation Engineers), Washington, USA, 99950T.
• Hamilton, O.K. & Breckon, T.P. (2016), Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow, 2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA, IEEE, Piscataway, NJ, 3439-3443.
• Webster, D.D. & Breckon, T.P. (2015), Improved raindrop detection using combined shape and saliency descriptors with scene context isolation, Proceedings of IEEE International Conference on Image Processing. Québec City, Canada, IEEE, Qubec City, 4376-4380.
• Kundegorski, M.E. & Breckon, T.P. (2015), Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery, Optics and Photonics for Counterterrorism, Crime Fighting and Defence. Toulouse, France, SPIE, Toulouse.
• Cavestany, P., Rodríguez, A.L., Martínez-Barberá, H. & Breckon, T.P. (2015), Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots, IEEE International Conference on Image Processing. Québec City, Canada, IEEE, Qubec City, 4927-4931.
• Mouton, A., Breckon, T.P., Flitton, G.T. & Megherbi, N. (2014), 3D object classification in baggage computed tomography imagery using randomised clustering forests, Proc. International Conference on Image Processing. IEEE, 5202-5206.
• Kundegorski, M.E. & Breckon, T.P. (2014), A photogrammetric approach for real-time 3D localization and tracking of pedestrians in monocular infrared imagery, 9253: Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence. SPIE, 1-16.
• Payen de La Garanderie, G. & Breckon, T.P. (2014), Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo, in Valstar, Michel, French, Andrew & Pridmore, Tony eds, Proceedings of the British Machine Vision Conference. BMVA Press, 417.1-417.12.
• Walger, D.J., Breckon, T.P., Gaszczak, A. & Popham, T. (2014), A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions, Proc. International Workshop on Computational Intelligence for Multimedia Understanding. IEEE, 1-5.
• Kurcius, J.J. & Breckon, T.P. (2014), Using Compressed Audio-visual Words for Multi-modal Scene Classification, Proc. International Workshop on Computational Intelligence for Multimedia Understanding. IEEE.
• Hamilton, O.K., Breckon, T.P., Bai, X. & Kamata, S. (2013), A Foreground Object based Quantitative Assessment of Dense Stereo Approaches for use in Automotive Environments, Proc. International Conference on Image Processing. IEEE, pp. 418-422.
• Breckon, T.P., Gaszczak, A., Han, J., Eichner, M.L. & Barnes, S.E. (2013), Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance, 8899: Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems. SPIE, 1-19.
• Megherbi, N., Breckon, T.P. & Flitton, G.T. (2013), Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection, 8901: Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence. SPIE, 1-8.
• Mioulet, L., Breckon, T.P., Mouton, A., Liang, H. & Morie, T. (2013), Gabor Features for Real-Time Road Environment Classification, Proc. International Conference on Industrial Technology. IEEE, 1117-1121.
• Mouton, A., Megherbi, N., Breckon, T.P., Van Slambrouck, K. & Nuyts, J. (2013), A Distance Weighted Method for Metal Artefact Reduction in CT, Proc. International Conference on Image Processing. IEEE, pp. 2334-2338.
• Turcsany, D., Mouton, A. & Breckon, T.P. (2013), Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words, Proc. International Conference on Industrial Technology. IEEE, 1140-1145.
• Mise, O. & Breckon, T.P. (2013), Image Super-Resolution applied to moving targets in high dynamics scenes, 8899: Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems. SPIE, 1-12.
• Han, J., Gaszczak, A., Maciol, R., Barnes, S.E. & Breckon, T.P. (2013), Human Pose Classification within the Context of Near-IR Imagery Tracking, 8901: Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence. SPIE, 1-10.
• Megherbi, N., Breckon, T.P., Flitton, G.T. & Mouton, A. (2013), Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection, 8901: Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence. SPIE, 1-7.
• Faria, J., Bagley, S., Rueger, S. & Breckon, T.P. (2013), Challenges of Finding Aesthetically Pleasing Images, Proc. International Workshop on Image and Audio Analysis for Multimedia Interactive Services. IEEE, 1-4.
• Chereau, R. & Breckon, T.P. (2013), Robust Motion Filtering as an Enabler to Video Stabilization for a Tele-operated Mobile Robot, 8897: Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII. SPIE, 1-17.
• Megherbi, N., Breckon, T.P., Flitton, G.T. & Mouton, A. (2012), Fully Automatic 3D Threat Image Projection: Application to Densely Cluttered 3D Computed Tomography Baggage Images, Proc. International Conference on Image Processing Theory, Tools and Applications. IEEE, 153-159.
• Megherbi, N., Han, J., Flitton, G.T. & Breckon, T.P. (2012), A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening, Proc. International Conference on Image Processing. IEEE, 3109-3112.
• Pinggera, P., Breckon, T.P. & Bischof, H. (2012), On Cross-Spectral Stereo Matching using Dense Gradient Features, Proc. British Machine Vision Conference. 526.1-526.12.
• Flitton, G., Breckon, T.P. & Megherbi, N. (2012), A 3D extension to cortex like mechanisms for 3D object class recognition, n/a: 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, IEEE, Providence RI, 3634-3641
• Breckon, T.P., Han, J. & Richardson, J. (2012), Consistency in Muti-modal Automated Target Detection using Temporally Filtered Reporting, 8542: Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI. 23:1-23:12.
• Carey, D., Shepherd, N., Kendall, C., Stone, N., Breckon, T.P. & Lloyd, G.R. (2012), Correlating Histology and Spectroscopy to Differentiate Pathologies of the Colon, Proc. Conference on Medical Image Understanding and Analysis. 243-248.
• Mouton, A., Megherbi, N., Flitton, G.T., Bizot, S. & Breckon, T.P. (2012), A Novel Intensity Limiting Approach to Metal Artefact Reduction in 3D CT Baggage Imagery, Proc. International Conference on Image Processing. IEEE, 2057-2060.
• Gaszczak, A., Breckon, T.P. & Han, J. (2011), Real-time People and Vehicle Detection from UAV Imagery, 7878: Proc. SPIE Conference Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques.
• Breszcz, M., Breckon, T.P. & Cowling, I. (2011), Real-time Mosaicing from Unconstrained Video Imagery for UAV Applications, Proc. 26th International Conference on Unmanned Air Vehicle Systems. 32.1-32.8.
• Heras, A.M., Breckon, T.P. & Tirovic, M. (2011), Video Re-sampling and Content Re-targeting for Realistic Driving Incident Simulation, Proc. 8th European Conference on Visual Media Production. sp-2.
• Chenebert, A., Breckon, T.P. & Gaszczak, A. (2011), A Non-temporal Texture Driven Approach to Real-time Fire Detection, Proc. International Conference on Image Processing. IEEE, 1781-1784.
• Bordes, L., Breckon, T.P., Katramados, I. & Kheyrollahi, A. (2011), Adaptive Object Placement for Augmented Reality Use in Driver Assistance Systems, Proc. 8th European Conference on Visual Media Production. sp-1.
• Katramados, I. & Breckon, T.P. (2011), Real-time Visual Saliency by Division of Gaussians, Proc. International Conference on Image Processing. IEEE, 1741-1744.
• Megherbi, N., Flitton, G.T. & Breckon, T.P. (2010), A Classifier based Approach for the Detection of Potential Threats in CT based Baggage Screening, Proc. International Conference on Image Processing. IEEE, 1833-1836.
• Kowaliszyn, M. & Breckon, T.P. (2010), Automatic Road Feature Detection and Correlation for the Correction of Consumer Satellite Navigation System Mapping, Proc. IET/ITS Conference on Road Transport Information and Control. IET, 2-9.
• Sokalski, J., Breckon, T.P. & Cowling, I. (2010), Automatic Salient Object Detection in UAV Imagery, Proc. 25th International Conference on Unmanned Air Vehicle Systems. 11.1-11.12.
• Flitton, G.T., Breckon, T.P. & Megherbi, N. (2010), Object Recognition using 3D SIFT in Complex CT Volumes, Proc. British Machine Vision Conference. 11.1-12.
• Katramados, I., Crumpler, S. & Breckon, T.P. (2009), Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis, Lecture Notes in Computer Science 5815: Proc. International Conference on Computer Vision Systems. Springer, 265-274.
• Wahren, K., Cowling, I., Patel, Y., Smith, P. & Breckon, T.P. (2009), Development of a Two-Tier Unmanned Air System for the MoD Grand Challenge, Proc. 24th International Conference on Unmanned Air Vehicle Systems. 13.1 - 13.9.
• Golebiowski, R., Breckon, T.P. & Flitton, G.T. (2009), Volumetric Representation for Interactive Video Editing, Proc. 6th European Conference on Visual Media Production. IET, 13.
• Breckon, T.P., Barnes, S.E., Eichner, M.L. & Wahren, K. (2009), Autonomous Real-time Vehicle Detection from a Medium-Level UAV, Proc. 24th International Conference on Unmanned Air Vehicle Systems. 29.1-29.9.
• Eichner, M. L. & Breckon, T.P. (2008), Integrated Speed Limit Detection and Recognition from Real-Time Video, Proc. IEEE Intelligent Vehicles Symposium. IEEE, 626-631.
• Eichner, M. L. & Breckon, T.P. (2008), Augmenting GPS Speed Limit Monitoring with Road Side Visual Information, Proc. IET/ITS Conference on Road Transport Information and Control. IET, 1-5.
• Han, J., Breckon, T.P., Randell, D.A. & Landini, G. (2008), Radicular cysts and odontogenic keratocysts epithelia classification using cascaded Haar classifiers, Proc. 12th Annual Conference on Medical Image Understanding and Analysis. 54-58.
• Rzeznik, J., Barnes, S.E. & Breckon, T.P. (2008), Gesture Recognition using a Laser Pointer, Proc. 5th European Conference on Visual Media Production. IET, SP-1.
• Desile, Q. & Breckon, T.P. (2008), 3D Colour Mesh Detail Enhancement Driven from 2D Texture Edge Information, Proc. 5th European Conference on Visual Media Production. IET, SP-4.
• Zirnhelt, S. & Breckon, T.P. (2007), Artwork Image Retrieval using Weighted Colour and Texture Similarity, Proc. 4th European Conference on Visual Media Production. IET, II-8.
• Breckon, T.P. (2007), 3D Measurement for Asset and Environment Authentication and Analysis, Proc. 4th International Conference on Condition Monitoring. British Institute of Non-Destructive Testing, 1-10.
• Eichner, M. L. & Breckon, T.P. (2007), Real-Time Video Analysis for Vehicle Lights Detection using Temporal Information, Proc. 4th European Conference on Visual Media Production. IET, I-9.
• Flitton, G.T. & Breckon, T.P. (2007), Considering Video as a Volume, Proc. 4th European Conference on Visual Media Production. IET, II-7.
• Li, X. & Breckon, T.P. (2007), Combining Motion Segmentation and Feature Based Tracking for Object Classification and Anomaly Detection, Proc. 4th European Conference on Visual Media Production. IET, I-6.
• Breckon, T.P. & Fisher, R.B. (2006), Direct Geometric Texture Synthesis and Transfer on 3D Meshes, Proc. 3rd European Conference on Visual Media Production. IET, 186.
• Breckon, T.P. & Fisher, R.B. (2005), Plausible 3D Colour Surface Completion using Non-parametric Techniques, Lecture Notes in Computer Science 3604: Proc. Mathematics of Surfaces XI Institute of Mathematics and its Applications. Springer-Verlag, 102-120.
• Breckon, T.P. & Fisher, R.B. (2005), Non-parametric 3D Surface Completion, Proc. Fifth International Conference on 3D Digital Imaging and Modeling. IEEE, 573-580.
• Breckon, T.P. & Fisher, R.B. (2005), A Non-parametric Approach to Realistic Surface Completion in 3D Environments, Proc. Postgraduate Research Conference in Electronics, Photonics, Communications and Networks, and Computing Science. EPSRC, 122.
• Breckon, T.P. & Fisher, R.B. (2004), Environment Authentication through 3D Structural Analysis, Lecture Notes in Computer Science 3211: Proc. International Conference on Image Analysis and Recognition. Springer-Verlag, 680-687.

Doctoral Thesis

• T.P. Breckon (2006). Completing Unknown Portions of 3D Scenes via 3D Visual Propogation. School of Informatics, University of Edinburgh. PhD.

Journal Article

• Gökstorp, S. & Breckon, T.P. (2021). Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video. The Visual Computer
• Wang, Q., Megherbi, N. & Breckon, T.P. (2020). A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes. Journal of X-Ray Science and Technology 28(3): 507-526.
• Wang, Q., Ismail, K.N. & Breckon, T.P. (2020). An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening. Journal of X-ray Science and Technology 28(1): 35-58.
• Zhang, W., Sun, C., Breckon, T.P. & Alshammari, N. (2019). Discrete Curvature Representations for Noise Robust Image Corner Detection. IEEE Transactions on Image Processing 28(9): 4444-4459.
• Podmore, J.J., Breckon, T.P., Aznan, N.K.N. & Connolly, J.D. (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 & Rehabilitation Engineering 27(4): 611-618.
• Maciel-Pearson, B.G., Akcay, S., Atapour-Abarghouei, A., Holder, C. & Breckon, T.P. (2019). Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments. Robotics and Automation Letters 4(4): 4116-4123.
• Mouton, A. & Breckon, T.P. (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 27(1): 51-72.
• Atapour-Abarghouei, Amir, Akcay, Samet, de La Garanderie, Grégoire Payen & Breckon, Toby P. (2019). Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer. Pattern Recognition 91: 232-244.
• Atapour-Abarghouei, A. & Breckon, T.P. (2018). A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion. Computers and Graphics 72: 39-58.
• Qian, C., Breckon, T.P. & Xu, Z. (2018). Clustering in pursuit of temporal correlation for human motion segmentation. Multimedia Tools and Applications 77(15): 19615-19631.
• Akcay, S., Kundegorski, M.E., Willcocks, C.G. & Breckon, T.P. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery. IEEE Transactions on Information Forensics & Security 13(9): 2203-2215.
• Zhang, W., Zhao, Y., Breckon, T.P. & L. Chen (2017). Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels. Pattern Recognition 63(8): 193-205.
• Chermak, L., Breckon, T.P., Flitton, G.T. & Megherbi, N. (2015). Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery. Imaging Science Journal
• Qian, Cheng, Breckon, Toby P. & Li, Hui (2015). Robust visual tracking via speedup multiple kernel ridge regression. Journal of Electronic Imaging 24(5): 053016
• Kriechbaumer, T., Blackburn, K., Breckon, T.P., 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.
• Mouton, A. & Breckon, T.P. (2015). A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening. Journal of X-Ray Science and Technology 23(5): 531-555.
• Flitton, G.T., Mouton, A. & Breckon, T.P. (2015). Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks. Pattern Recognition 48(8): 2489-2499.
• Breszcz, M. & Breckon, T.P. (2015). Real-time Construction and Visualization of Drift-Free Video Mosaics from Unconstrained Camera Motion. The Journal of Engineering 2015(16): 1-12.
• Mouton, A. & Breckon, T.P. (2015). Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening. Pattern Recognition 48(6): 1961-1978.
• Magnabosco, M. & Breckon, T.P. (2013). Cross-Spectral Visual Simultaneous Localization And Mapping (SLAM) with Sensor Handover. Robotics and Autonomous Systems 63(2): 195-208.
• Flitton, G., Breckon, T.P. & 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
• Mouton, A., Megherbi, N., Van Slambrouck, K., Nuyts, J. & Breckon, T.P. (2013). An Experimental Survey of Metal Artefact Reduction in Computed Tomography. Journal of X-Ray Science and Technology 21(2): 193-226.
• Mroz, F. & Breckon, T.P. (2012). An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment. EURASIP Journal on Image and Video Processing 2012: 13.
• Han, J., Breckon, T.P., Randell, D.A. & 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.
• Kheyrollahi, A. & Breckon, T.P. (2012). Automatic Real-time Road Marking Recognition Using a Feature Driven Approach. Machine Vision and Applications 23(1): 123-133.
• Breckon, T.P. & Fisher, R.B. (2012). A hierarchical extension to 3D non-parametric surface relief completion. Pattern Recognition 45(1): 172-185.
• Breckon, T.P., Jenkins, K.W. & Sonkoly, P. (2011). Realizing Perceptive Virtual Reality Imaging Applications on Conventional PC Hardware. Imaging Science Journal 59(1): 1-7.
• Tang, I. & Breckon, T.P. (2011). Automatic Road Environment Classification. IEEE Transactions on Intelligent Transportation Systems 12(2): 476-484.
• Landini, G., Randell, D.A., Breckon, T.P. & 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.P. & Fisher, R.B. (2008). Three-Dimensional Surface Relief Completion Via Nonparametric Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(12): 2249-2255.
• Breckon, T.P. & Fisher, R.B. (2005). Amodal Volume Completion: 3D Visual Completion. Computer Vision and Image Understanding 99(3): 499-526.