All Outputs (19)

Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network (2024)
Journal Article
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

Multiple sclerosis (MS) is a chronic neurological disorder that targets the central nervous system, causing demyelination and neural disruption, which can include retinal nerve damage leading to visual disturbances. The purpose of this study is to de... Read More about Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network.

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics (2024)
Journal Article
Yucer, S., Atapour-Abarghouei, A., Al Moubayed, N., & Breckon, T. P. (online). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. arXiv,

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges... Read More about Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
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

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms (2023)
Journal Article
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

Purpose : A deep learning framework to differentiate glaucomatous optic disc changes (GON) from non-glaucomatous optic neuropathy-related disc changes (NGON). Design : Cross-sectional study. Method : A deep-learning system was trained, validated, and... Read More about Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms.

On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures (2021)
Presentation / Conference Contribution
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

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks. Whilst the reported performance of these a... Read More about On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures.

Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation (2019)
Book Chapter
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

Even though obtaining 3D information has received significant attention in scene capture systems in recent years, there are currently numerous challenges within scene depth estimation which is one of the fundamental parts of any 3D vision system focu... Read More about Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation.

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019)
Presentation / Conference Contribution
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

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data... Read More about Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection.

To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation (2019)
Presentation / Conference Contribution
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

Robust three-dimensional scene understanding is now an ever-growing area of research highly relevant in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based model capabl... Read More about To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation.

Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior (2019)
Presentation / Conference Contribution
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

Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quant... Read More about Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior.

Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments (2019)
Journal Article
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

Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search... Read More about Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments.

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification (2019)
Presentation / Conference Contribution
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

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subj... Read More about Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.

Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach (2019)
Presentation / Conference Contribution
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

Style Augmentation: Data Augmentation via Style Randomization (2019)
Presentation / Conference Contribution
Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019). Style Augmentation: Data Augmentation via Style Randomization.

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of Convolutional Neural Networks (CNN) over both classification and regression based tasks. During training, style augmentat... Read More about Style Augmentation: Data Augmentation via Style Randomization.

Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer (2018)
Presentation / Conference Contribution
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

Monocular depth estimation using learning-based approaches has become promising in recent years. However, most monocular depth estimators either need to rely on large quantities of ground truth depth data, which is extremely expensive and difficult t... Read More about Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer.

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018)
Presentation / Conference Contribution
Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. Presented at 14th Asian Conference on Computer Vision (ACCV)., Perth, Australia

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While... Read More about GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Presentation / Conference Contribution
Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion. Presented at International Conference Image Analysis and Recognition, Póvoa de Varzim, Portugal

We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color i... Read More about Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion.

A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion (2018)
Journal Article
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

Despite significant research focus on 3D scene capture systems, numerous unresolved challenges remain in relation to achieving full coverage scene depth estimation which is the key part of any modern 3D sensing system. This has created an area of res... Read More about A Comparative Review of Plausible Hole Filling Strategies in the Context of Scene Depth Image Completion.

DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation (2017)
Presentation / Conference Contribution
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

We address plausible hole filling in depth images in a computationally lightweight methodology that leverages recent advances in semantic scene segmentation. Firstly, we perform such segmentation over a co-registered color image, commonly available f... Read More about DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation.

Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery (2016)
Presentation / Conference Contribution
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

We address the problem of hole filling in RGB-D (color and depth) images, obtained from either active or stereo based sensing, for the purposes of object removal and missing depth estimation. This is performed independently on the low frequency depth... Read More about Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery.