Staff profile

Background

• Depth Estimation and 3D Reconstruction
• Domain Adaptation and Data Augmentation
• Image Processing and Computer Vision
• Machine Learning / Deep Learning
• Multi-Task Learning and Neural Architecture Search
• Robotic Navigation and Autonomy
• Scene Understanding and Image Analysis
• Semantic Segmentation and Object Detection
• Text Ranking and Classification
• Topic Modelling and Sentiment Analysis

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.
• 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. (in press). FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment. Presented at Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025, Music City Center, Nashville TN.
• 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 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.
• 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
• A Modified PSO Method Enhanced with Fuzzy Inference System for Solving the Planar Graph Coloring Problem
  
  Erfani, M., Ghanizadeh, A., Atapour-Abarghouei, A., Sinaie, S., & Shamsuddin, S. M. (2010). A Modified PSO Method Enhanced with Fuzzy Inference System for Solving the Planar Graph Coloring Problem. Presented at International Conference on Artificial Intelligence.
• A Robust Fuzzy and Cellular Learning Automata Edge Detection and Enhancement Method
  
  Ghanizadeh, A., Sinaie, S., Atapour-Abarghouei, A., Mozafari, E., & Shamsuddin, S. M. (2010). A Robust Fuzzy and Cellular Learning Automata Edge Detection and Enhancement Method. Presented at International Conference on Image Processing, Computer Vision, & Pattern Recognition, Las Vegas, Nevada.
• A Survey of Pattern Recognition Applications in Cancer Diagnosis
  
  Atapour-Abarghouei, A., Ghanizadeh, A., Sinaie, S., & Shamsuddin, S. M. (2009). A Survey of Pattern Recognition Applications in Cancer Diagnosis. Presented at International Conference on Soft Computing and Pattern Recognition, Malacca. https://doi.org/10.1109/socpar.2009.93

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.

Masters Thesis

• A Novel Solution to Travelling Salesman Problem using Fuzzy Sets, Gravitational Search Algorithm, and Genetic Algorithm
  
  Atapour-Abarghouei, A. (2010). A Novel Solution to Travelling Salesman Problem using Fuzzy Sets, Gravitational Search Algorithm, and Genetic Algorithm [Dissertation]. Awarding Organisation: Unknown.