Staff profile

Background

Amir Atapour-Abarghouei is an Associate Professor in the Department of Computer Science at Durham University and Co-Director of the Wolfson Research Institute for Health & Wellbeing, working on dependable computer vision and machine learning that operate robustly outside the lab, with applications spanning biodiversity monitoring, precision agriculture and pollinator health, public health and vector surveillance, trustworthy medical imaging, security/surveillance and sustainable transport. He has led or co-leds AI/vision on major research programmes, including Horizon Europe BioDiMoBot (Autonomous Long-time Aquatic Biodiversity Monitoring Robot, EIC Pathfinder Sensorbees (bio-hybrid environmental sensing), EPSRC IAA 360° video anomaly detection, DSTL persistent wide-area surveillance and an Innovate UK/Evergreen Health Solutions KTP on real-world dermatology AI. He is the Project Lead for the MoniRail KTP (£299,744), which embeds Durham-led AI into MoniRail’s in-service monitoring to predict track and vehicle degradation, optimise condition-based maintenance and enable robust positioning in GNSS-denied settings. 

Methodologically, his group advances efficiency of learning, applications of computer vision and deep learning in robotics, robustness under distribution shift (domain adaptation, multi-task learning, label-efficient training), bias-aware medical AI, and perception in difficult sensing regimes (thermal/infrared, panoramic/360°), with open field-ready pipelines. His work also focuses on semi-supervised anomaly/rare-event detection with his GANomaly/Skip-GANomaly approaches having been adopted in Intel OpenVINO/GETI product line and cited by 40+ patents as the underlying anomaly detection method.  He serves as Associate Editor for IEEE Transactions on Cognitive and Developmental Systems, Area Chair for BMVC and IROS, co-organiser of the CVPR NAS workshop, Chair of the BMVA Computer Vision Summer School at Durham and a member of the BMVA Executive Committee.

• Neuromorphic perception and control: event-based vision, spiking neural networks.
• Efficient autonomy: modular full-stack pipelines for SLAM, teach-and-repeat navigation, obstacle avoidance, path planning and real-time decision-making on edge/embedded hardware.
• Robust perception under shift: domain adaptation, label-/data-efficient learning, multi-task learning and temporal consistency for depth/segmentation/optical flow in adverse or unusual sensing regimes (thermal/IR, panoramic/360°).
• Anomaly & rare-event detection: semi-/self-supervised generative methods, spatial and spatiotemporal anomaly localisation for surveillance, industrial inspection and medical imaging.
• Multimodal sensor fusion: event+IMU+polarisation and classical vision fusion for reliable state estimation and guidance.
• Multimodal representation learning: cross-modal alignment and grounding (vision–language–inertial), shared latent spaces and contrastive/self-supervised objectives.
• Continual & self-supervised learning: Strategies to mitigate catastrophic forgetting and task-agnostic lifelong learning for autonomous platforms.
• Hardware-aware ML: lightweight NAS, pruning/quantisation, latency/energy-constrained model design for real-time deployment on CPUs/GPUs/neuromorphic chips.
• Time-series modelling & streaming analytics: multiscale forecasting and change-point detection.
• Trustworthy ML: bias detection/mitigation, robustness explainability, and safety validation for high-stakes applications (healthcare, transport).
• Mapping & positioning: event-based VIO/SLAM, map alignment (incl. GNN/diffusion approaches) and GNSS-denied localisation (e.g., tunnels).
• Scalable training systems: reproducible pipelines, large-scale experimentation, and open field-ready software stacks (e.g., ROS integration).
• Resource-efficient neural architectures: principled designs for fast stable convergence and high accuracy per flop and spiking/temporal operators tuned for tight memory/latency budgets.

Chapter in book

• Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation
  
  Atapour-Abarghouei, A., & Breckon, T. (2019). Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation. In P. L. Rosin, Y. Lai, L. Shao, & Y. Liu (Eds.), RGB-D image analysis and processing. (pp. 15-50). Springer Verlag. https://doi.org/10.1007/978-3-030-28603-3_2
• Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
  
  Payen de La Garanderie, G., Atapour Abarghouei, A., & Breckon, T. P. (2018). Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 : 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XII. (pp. 812-830). Springer Verlag. https://doi.org/10.1007/978-3-030-01261-8_48

Conference Paper

• Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
  
  E, W., Yuan, C., Sun, Y., Gaus, Y., Atapour-Abarghouei, A., & Breckon, T. (in press). Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving. In Proceedings of the International Conference on Robotics and Automation 2025. IEEE Canada.
• Long-term Reproducibility for Neural Architecture Search
  
  Towers, D., Forshaw, M., Atapour-Abarghouei, A., & McGough, A. S. (in press). Long-term Reproducibility for Neural Architecture Search. Presented at IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops, New Orleans, USA.
• DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
  
  Sun, Y., Li, L., E, W., Atapour-Abarghouei, A., & Breckon, T. (in press). DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions. In Proceedings of the International Joint Conference on Neural Networks. IEEE.
• DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics
  
  Chen, S., Thenius, R., Arvin, F., & Atapour-Abarghouei, A. (in press). DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics. Presented at The 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025), Hangzhou, China.
• Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
  
  Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (in press). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA.
• BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
  
  Xiang, S., Chen, S., & Atapour-Abarghouei, A. (in press). BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion. Presented at The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China.
• FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
  
  Han, R., Zhou, K., Atapour-Abarghouei, A., Liang, X., & Shum, H. P. H. (2025). FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 6008-6017). https://doi.org/10.1109/CVPRW67362.2025.00599
• Beyond Syntax: How Do LLMs Understand Code?
  
  North, M., Atapour-Abarghouei, A., & Bencomo, N. (2025). Beyond Syntax: How Do LLMs Understand Code? In 2025 IEEE/ACM 47th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) (pp. 86-90). IEEE. https://doi.org/10.1109/ICSE-NIER66352.2025.00023
• SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM
  
  Chen, S., Zhang, H., Atapour-Abarghouei, A., & Shum, H. P. H. (2025). SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM. In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 461-471). IEEE. https://doi.org/10.1109/WACV61041.2025.00055
• Insights from the Use of Previously Unseen Neural Architecture Search Datasets
  
  Geada, R., Towers, D., Forshaw, M., Atapour-Abarghouei, A., & Mcgough, A. S. (2024). Insights from the Use of Previously Unseen Neural Architecture Search Datasets. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 22541-22550). IEEE. https://doi.org/10.1109/CVPR52733.2024.02127
• Code Gradients: Towards Automated Traceability of LLM-Generated Code
  
  North, M., Atapour-Abarghouei, A., & Bencomo, N. (2024). Code Gradients: Towards Automated Traceability of LLM-Generated Code. In 2024 IEEE 32nd International Requirements Engineering Conference (RE) (pp. 321-329). IEEE. https://doi.org/10.1109/RE59067.2024.00038
• Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
  
  Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE. https://doi.org/10.1109/ijcnn60899.2024.10650732
• FEGR: Feature Enhanced Graph Representation Method for Graph Classification
  
  Abushofa, M., Atapour-Abarghouei, A., Forshaw, M., & McGough, A. S. (2024, March 15). FEGR: Feature Enhanced Graph Representation Method for Graph Classification. Presented at 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kusadasi, Turkey. https://doi.org/10.1145/3625007.3627600
• MxT: Mamba x Transformer for Image Inpainting
  
  Chen, S., Atapour-Abarghouei, A., Zhang, H., & Shum, H. P. H. (2024). MxT: Mamba x Transformer for Image Inpainting. In Proceedings of the 2024 British Machine Vision Conference. British Machine Vision Association.
• Predicting the Performance of a Computing System with Deep Networks
  
  Cengiz, M., Forshaw, M., Atapour-Abarghouei, A., & McGough, A. S. (2023). Predicting the Performance of a Computing System with Deep Networks. In ICPE ’23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (pp. 91-98). ACM. https://doi.org/10.1145/3578244.3583731
• Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
  
  Battle, M. L., Atapour-Abarghouei, A., & McGough, A. S. (2023, January 26). Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets. Presented at 2022 IEEE International Conference on Big Data, Osaka, Japan. https://doi.org/10.1109/bigdata55660.2022.10020820
• Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
  
  Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). 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). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPRW59228.2023.00301
• Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
  
  Bevan, P. J., & Atapour-Abarghouei, A. (2022). Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification. In K. Kamnitsas, L. Koch, M. Islam, Z. Xu, J. Cardoso, Q. Doi, N. Rieke, & S. Tsaftaris (Eds.), DART 2022: Domain Adaptation and Representation Transfer (pp. 1-11). Springer Verlag. https://doi.org/10.1007/978-3-031-16852-9_1
• A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
  
  Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926917
• Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
  
  Bevan, P., & Atapour-Abarghouei, A. (2022). Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of Machine Learning Research (pp. 1874-1892). ML Research Press.
• Transforming Fake News: Robust Generalisable News Classification Using Transformers
  
  Blackledge, C., & Atapour-Abarghouei, A. (2021, December 15). Transforming Fake News: Robust Generalisable News Classification Using Transformers. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA. https://doi.org/10.1109/bigdata52589.2021.9671970
• Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking
  
  Carrell, S., & Atapour-Abarghouei, A. (2021, December 15). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA. https://doi.org/10.1109/bigdata52589.2021.9671378
• Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
  
  Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021, December 15). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA. https://doi.org/10.1109/bigdata52589.2021.9671386
• On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures
  
  Poyser, M., Atapour-Abarghouei, A., & Breckon, T. (2021). On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. Presented at 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy. https://doi.org/10.1109/icpr48806.2021.9412455
• “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving
  
  Stelling, J., & Atapour-Abarghouei, A. (2021). “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA. https://doi.org/10.1109/bigdata52589.2021.9672033
• Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
  
  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. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ijcnn.2019.8851808
• Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior
  
  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. (pp. 4295-4299). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icip.2019.8803551
• To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation
  
  Atapour-Abarghouei, A., & Breckon, T. P. (2019). 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) (pp. 183-193). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/3dv.2019.00029
• Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
  
  Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). 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 (pp. 1-8). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ijcnn.2019.8852227
• Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach
  
  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. Institute of Electrical and Electronics Engineers.
• Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions
  
  Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2019). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. In 2019 IEEE International Conference on Big Data (Big Data). (pp. 5336-5345). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/bigdata47090.2019.9005545
• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
  
  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. (pp. 622-637). Springer Verlag. https://doi.org/10.1007/978-3-030-20893-6_39
• Volenti non fit injuria: Ransomware and its Victims
  
  Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2019). Volenti non fit injuria: Ransomware and its Victims. Presented at 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA. https://doi.org/10.1109/bigdata47090.2019.9006298
• Style Augmentation: Data Augmentation via Style Randomization
  
  Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019, January 1). Style Augmentation: Data Augmentation via Style Randomization. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision, Long Beach, CA, USA.
• A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation
  
  Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2019). A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation. In Proceedings of 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9005540
• Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion
  
  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. ter H. Romeny (Eds.), Image analysis and recognition : 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018 ; proceedings. (pp. 306-314). Springer Verlag. https://doi.org/10.1007/978-3-319-93000-8_35
• Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer
  
  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 (pp. 2800-2810). IEEE/CVF. https://doi.org/10.1109/CVPR.2018.00296
• DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation
  
  Atapour-Abarghouei, A., & Breckon, T. (2017). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation. In Proc. British Machine Vision Conference (pp. 208.1-208.13). BMVA. https://doi.org/10.5244/C.31.58
• Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery
  
  Atapour-Abarghouei, A., de La Garanderie, G. P., & Breckon, T. P. (2016). Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery. In Proc. Int. Conf. on Pattern Recognition (pp. 2813-2818). IEEE. https://doi.org/10.1109/ICPR.2016.7900062

Doctoral Thesis

• Immaculate Depth Perception: Recovering 3D Scene Information via Depth Completion and Prediction
  
  Atapour-Abarghouei, A. (2019). Immaculate Depth Perception: Recovering 3D Scene Information via Depth Completion and Prediction [Thesis]. Durham University. http://etheses.dur.ac.uk/13310/

Journal Article

• Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network
  
  Bolton, R. C., Kafieh, R., Ashtari, F., & Atapour-Abarghouei, A. (2024). Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network. IEEE Access, 12, 62975-62985. https://doi.org/10.1109/access.2024.3395995
• HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention
  
  Chen, S., Atapour-Abarghouei, A., & Shum, H. P. H. (2024). HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention. IEEE Transactions on Multimedia, 26, 7649-7660. https://doi.org/10.1109/TMM.2024.3369897
• INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
  
  Chen, S., Atapour-Abarghouei, A., Ho, E. S., & Shum, H. P. (2023). INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network. Software Impacts, 17, Article 100517. https://doi.org/10.1016/j.simpa.2023.100517
• Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms
  
  Vali, M., Mohammadi, M., Zarei, N., Samadi, M., Atapour-Abarghouei, A., Supakontanasan, W., Suwan, Y., Subramanian, P. S., Miller, N. R., Kafieh, R., & Fard, M. A. (2023). Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms. American Journal of Ophthalmology, 252, 1-8. https://doi.org/10.1016/j.ajo.2023.02.016
• Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
  
  Maciel-Pearson, B., Akcay, S., Atapour-Abarghouei, A., Holder, C., & Breckon, T. (2019). Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments. IEEE ROBOTICS AND AUTOMATION LETTERS, 4(4), 4116-4123. https://doi.org/10.1109/lra.2019.2930496
• Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer
  
  Atapour-Abarghouei, A., Akcay, S., de La Garanderie, G. P., & Breckon, T. P. (2019). Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer. Pattern Recognition, 91, 232-244. https://doi.org/10.1016/j.patcog.2019.02.010
• A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion
  
  Atapour-Abarghouei, A., & Breckon, T. (2018). A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion. Computers and Graphics, 72, 39-58. https://doi.org/10.1016/j.cag.2018.02.001
• Iris Segmentation using an Edge Detector based on Fuzzy Sets Theory and Cellular Learning Automata
  
  Ghanizadeh, A., Atapour-Abarghouei, A., Sinaie, S., Saad, P., & Shamsuddin, S. M. (2011). Iris Segmentation using an Edge Detector based on Fuzzy Sets Theory and Cellular Learning Automata. Applied Optics, 50(19), 3191-3200. https://doi.org/10.1364/ao.50.003191
• Advances of Soft Computing Methods in Edge Detection
  
  Atapour-Abarghouei, A., Ghanizadeh, A., & Shamsuddin, S. M. (2009). Advances of Soft Computing Methods in Edge Detection. International Journal of Advances in Soft Computing and Its Applications, 1(2), 162-203.