Staff profile
Professor Toby Breckon
Professor
BSc PhD CEng CSci ASIS FRPS FIET FBCS
Affiliation | Telephone |
---|---|
Professor in the Department of Computer Science | +44 (0) 191 33 42396 |
Professor in the Department of Engineering | |
Fellow of the Wolfson Research Institute for Health and Wellbeing | +44 (0) 191 33 42396 |
Biography
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.
Research interests
- autonomous sensing
- computer vision
- image processing
- machine learning
- robotic sensing
Publications
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
- Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024). U3DS3 : Unsupervised 3D Semantic Scene Segmentation. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (3747-3756). https://doi.org/10.1109/WACV57701.2024.00372
- 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.
- 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
- 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
- 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
- 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
- Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023). 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. https://doi.org/10.1109/ICCV51070.2023.01341
- 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
- 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.
- Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022). Does lossy image compression affect racial bias within face recognition?.
- 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)
- Groom, M., & Breckon, T. (2022). On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision.
- Isaac-Medina, B., Willcocks, C., & Breckon, T. (2022). Multi-view Vision Transformers for Object Detection.
- Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.
- 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
- 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
- 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
- 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
- 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.
- 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
- Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021). Autoencoders Without Reconstruction for Textural Anomaly Detection. . https://doi.org/10.1109/ijcnn52387.2021.9533804
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Barker, J., & Breckon, T. (2021). PANDA: Perceptually Aware Neural Detection of Anomalies. . https://doi.org/10.1109/ijcnn52387.2021.9534399
- 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
- Alsehaim, A., & Breckon, T. (2020). Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification.
- 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
- 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.
- 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. (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
- 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
- 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
- Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019). Style Augmentation: Data Augmentation via Style Randomization.
- 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
- 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
- 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
- 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
- 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
- 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
- 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., 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
- 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.
- 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 Proc. Computer Vision and Pattern Recognition (2800-2810). https://doi.org/10.1109/CVPR.2018.00296
- Holder, C., & Breckon, T. (2018). Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments. In Proc. Int. Conf. Image Analysis and Recognition (427-434). https://doi.org/10.1007/978-3-319-93000-8_48
- 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. In Proc. Int. Conf. on Image Processing (3413-3417). https://doi.org/10.1109/ICIP.2018.8451453
- Dong, Z., Kamata, S., & Breckon, T. (2018). Infrared Image Colorization Using S-Shape Network. In Proc. Int. Conf. on Image Processing (2242-2246). https://doi.org/10.1109/ICIP.2018.8451230
- Alshammari, N., Akcay, S., & Breckon, T. (2018). On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding. In Proc. Intelligent Vehicles Symposium (1027-1032). https://doi.org/10.1109/IVS.2018.8500664
- Dunnings, A., & Breckon, T. (2018). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. In Proc. Int. Conf. on Image Processing (1558-1562). https://doi.org/10.1109/ICIP.2018.8451657
- Loveday, M., & Breckon, T. (2018). On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling. In Proc. Computer Vision and Pattern Recognition Workshops (1267-1276). https://doi.org/10.1109/CVPRW.2018.00164
- Lin, K., & Breckon, T. (2018). Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras. In Proc. Int. Conf. Image Analysis and Recognition (315-325). https://doi.org/10.1007/978-3-319-93000-8_36
- Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. In Proc. Towards Autonomous Robotic Systems Conference (147-158). https://doi.org/10.1007/978-3-319-96728-8_13
- Holder, C., & Breckon, T. (2018). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction. In Proc. Intelligent Vehicles Symposium (2104-2110). https://doi.org/10.1109/IVS.2018.8500526
- 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
- Atapour-Abarghouei, A., & Breckon, T. (2017). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation. In Proc. British Machine Vision Conference (208.1-208.13). https://doi.org/10.5244/C.31.58
- Akcay, S., & Breckon, T. (2017). An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery. In Proc. Int. Conf. on Image Processing (1337-1341). https://doi.org/10.1109/ICIP.2017.8296499
- Wu, R., Kamata, S., & Breckon, T. (2017). Face Recognition via Deep Sparse Graph Neural Networks. In Proc. British Machine Vision Conference Workshops (1-8)
- Maciel-Pearson, B., & Breckon, T. (2017). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. In Proc. Conf. on Robotics and Autonomous Systems - Robots that Work Among Us Workshop (1-3)
- 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
- 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
- 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.), Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-16). https://doi.org/10.1117/12.2241106
- 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. In Proc. Int. Conf. on Pattern Recognition (2813-2818). https://doi.org/10.1109/ICPR.2016.7900062
- Katramados, I., & Breckon, T. (2016). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. In Proc. Int. Conf. on Image Processing (300-304). https://doi.org/10.1109/ICIP.2016.7532367
- 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 Proc. Int. Conf. on Image Processing (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 Proc. Int. Conf. on Image Processing (3319-3323). https://doi.org/10.1109/ICIP.2016.7532974
- 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 Proc. Int. Conf. on Imaging for Crime Detection and Prevention (12 (6 .)-12 (6 .)(1)). https://doi.org/10.1049/ic.2016.0080
- Hamilton, O., & Breckon, T. (2016). Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow. In Proc. Int. Conf. on Image Processing (3439-3443). https://doi.org/10.1109/ICIP.2016.7532998
- Kundegorski, M., & Breckon, T. (2015). Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery. In Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-12). https://doi.org/10.1117/12.2195050
- Webster, D., & Breckon, T. (2015). Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation. In Proc. Int. Conf. on Image Processing (4376-4380). https://doi.org/10.1109/ICIP.2015.7351633
- Cavestany, P., Rodríguez, A., Rodriguez, A., Martínez-Barberá, H., Martinez-Barbera, H., & Breckon, T. (2015). Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots. In Proc. Int. Conf. on Image Processing (4927-4931). https://doi.org/10.1109/ICIP.2015.7351744
- 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
- Kurcius, J., & Breckon, T. (2014). Using Compressed Audio-visual Words for Multi-modal Scene Classification. In Proc. International Workshop on Computational Intelligence for Multimedia Understanding (1-5). 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 Proc. International Workshop on Computational Intelligence for Multimedia Understanding (1-5). 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 Proc. Int. Conf. on Image Processing (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 Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-16). https://doi.org/10.1117/12.2065673
- 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
- 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
- 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
- Turcsany, D., Mouton, A., & Breckon, T. (2013). Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words. In Proc. Int. Conf. on Industrial Technology (1140-1145). 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. In Proc. Int. Conf. on Industrial Technology (1117-1121). https://doi.org/10.1109/ICIT.2013.6505829
- Han, J., Gaszczak, A., Maciol, R., Barnes, S., & Breckon, T. (2013). Human Pose Classification within the Context of Near-IR Imagery Tracking. In Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-10). https://doi.org/10.1117/12.2028375
- Mise, O., & Breckon, T. (2013). Image Super-Resolution Applied to Moving Targets in High Dynamics Scenes. In Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems (1-12). 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. In Proc. Int. Conf. on Image Processing (418-422). https://doi.org/10.1109/ICIP.2013.6738086
- Breckon, T., Gaszczak, A., Han, J., Eichner, M., & Barnes, S. (2013). Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance. In Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems (1-19). https://doi.org/10.1117/12.2028340
- Faria, J., Bagley, S., Rueger, S., & Breckon, T. (2013). Challenges of Finding Aesthetically Pleasing Images. In Proc. International Workshop on Image and Audio Analysis for Multimedia Interactive Services (1-4). https://doi.org/10.1109/WIAMIS.2013.6616162
- Chereau, R., & Breckon, T. (2013). Robust Motion Filtering as an Enabler to Video Stabilization for a Tele-operated Mobile Robot. In Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII (1-17). https://doi.org/10.1117/12.2028360
- 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
- 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
- Carey, D., Shepherd, N., Kendall, C., Stone, N., Breckon, T., & Lloyd, G. (2012). Correlating Histology and Spectroscopy to Differentiate Pathologies of the Colon. In Proc. Conference on Medical Image Understanding and Analysis (243-248)
- Breckon, T., Han, J., & Richardson, J. (2012). Consistency in Muti-modal Automated Target Detection using Temporally Filtered Reporting. In Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI (23:1-23:12). https://doi.org/10.1117/12.974559
- Pinggera, P., Breckon, T., & Bischof, H. (2012). On Cross-Spectral Stereo Matching using Dense Gradient Features. In Proc. British Machine Vision Conference (526.1-526.12). https://doi.org/10.5244/C.26.103
- Bordes, L., Breckon, T., Katramados, I., & Kheyrollahi, A. (2011). Adaptive Object Placement for Augmented Reality Use in Driver Assistance Systems. In Proc. 8th European Conference on Visual Media Production (sp-1)
- Heras, A., Breckon, T., & Tirovic, M. (2011). Video Re-sampling and Content Re-targeting for Realistic Driving Incident Simulation. In Proc. 8th European Conference on Visual Media Production (sp-2)
- Chenebert, A., Breckon, T., & Gaszczak, A. (2011). A Non-temporal Texture Driven Approach to Real-time Fire Detection. In Proc. Int. Conf. on Image Processing (1781-1784). https://doi.org/10.1109/ICIP.2011.6115796
- Gaszczak, A., Breckon, T., & Han, J. (2011). Real-time People and Vehicle Detection from UAV Imagery. . https://doi.org/10.1117/12.876663
- Katramados, I., & Breckon, T. (2011). Real-time Visual Saliency by Division of Gaussians. In Proc. Int. Conf. on Image Processing (1741-1744). https://doi.org/10.1109/ICIP.2011.6115785
- Breszcz, M., Breckon, T., & Cowling, I. (2011). Real-time Mosaicing from Unconstrained Video Imagery for UAV Applications. In Proc. 26th Int. Conf. on Unmanned Air Vehicle Systems (32.1-32.8)
- Flitton, G., Breckon, T., & Megherbi, N. (2010). Object Recognition using 3D SIFT in Complex CT Volumes. . https://doi.org/10.5244/c.24.11
- 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
- Sokalski, J., Breckon, T., & Cowling, I. (2010). Automatic Salient Object Detection in UAV Imagery.
- Kowaliszyn, M., & Breckon, T. (2010). Automatic Road Feature Detection and Correlation for the Correction of Consumer Satellite Navigation System Mapping. In Proc. IET/ITS Conf. on Road Transport Information and Control (2-9). https://doi.org/10.1049/cp.2010.0397
- Wahren, K., Cowling, I., Patel, Y., Smith, P., & Breckon, T. (2009). Development of a Two-Tier Unmanned Air System for the MoD Grand Challenge.
- Breckon, T., Barnes, S., Eichner, M., & Wahren, K. (2009). Autonomous Real-time Vehicle Detection from a Medium-Level UAV.
- Golebiowski, R., Breckon, T., & Flitton, G. (2009). Volumetric Representation for Interactive Video Editing. In Proc. 6th European Conference on Visual Media Production (13)
- Katramados, I., Crumpler, S., & Breckon, T. (2009). Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis. In Proc. Int. Conf. on Computer Vision Systems (265--274). 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
- Eichner, M., & Breckon, T. (2008). Augmenting GPS Speed Limit Monitoring with Road Side Visual Information.
- Han, J., Breckon, T., Randell, D., & Landini, G. (2008). Radicular Cysts and Odontogenic Keratocysts Epithelia Classification using Cascaded Haar Classifiers. In Proc. 12th Annual Conference on Medical Image Understanding and Analysis (54-58)
- Eichner, M. L., & Breckon, T. (2008). Integrated Speed Limit Detection and Recognition from Real-Time Video. In Proc. IEEE Intelligent Vehicles Symposium (626-631). https://doi.org/10.1109/IVS.2008.4621285
- Rzeznik, J., Barnes, S., & Breckon, T. (2008). Gesture Recognition using a Laser Pointer. In Proc. 5th European Conference on Visual Media Production (SP-1). https://doi.org/10.1049/cp%3A20081085
- Breckon, T. (2007). 3D Measurement for Asset and Environment Authentication and Analysis.
- Flitton, G., & Breckon, T. (2007). Considering Video as a Volume. In Proc. 4th European Conference on Visual Media Production (II-7). https://doi.org/10.1049/cp%3A20070063
- Eichner, M. L., & Breckon, T. (2007). Real-Time Video Analysis for Vehicle Lights Detection using Temporal Information. In Proc. 4th European Conference on Visual Media Production (I-9). https://doi.org/10.1049/cp%3A20070047
- Zirnhelt, S., & Breckon, T. (2007). Artwork Image Retrieval using Weighted Colour and Texture Similarity. In Proc. 4th European Conference on Visual Media Production (II-8). https://doi.org/10.1049/cp%3A20070064
- Li, X., & Breckon, T. (2007). Combining Motion Segmentation and Feature Based Tracking for Object Classification and Anomaly Detection. In Proc. 4th European Conference on Visual Media Production (I-6). https://doi.org/10.1049/cp%3A20070044
- 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
Journal Article
- 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, https://doi.org/10.1109/TAI.2024.3379940
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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