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Outputs (211)

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019)
Presentation / Conference Contribution
Akcay, A., Atapour-Abarghouei, A., & Breckon, T. P. (2019, July). Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection. Presented at Proc. Int. Joint Conference on Neural Networks, Budapest, Hungary

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.

The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery (2019)
Presentation / Conference Contribution
Bhowmik, N., Wang, Q., Gaus, Y., Szarek, M., & Breckon, T. (2019, September). The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery. Presented at British Machine Vision Conference Workshops, Cardiff, Wales, UK

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, September). To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation. Presented at International Conference on 3D Vision, Quebec

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, September). Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior. Presented at IEEE International Conference on Image Processing, Taipei, Taiwen

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.

A Ranking based Attention Approach for Visual Tracking (2019)
Presentation / Conference Contribution
Peng, S., Kamata, S., & Breckon, T. (2019, September). A Ranking based Attention Approach for Visual Tracking. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

Correlation filters (CF) combined with pre-trained convolutional neural network (CNN) feature extractors have shown an admirable accuracy and speed in visual object tracking. However, existing CNN-CF based methods still suffer from the background int... Read More about A Ranking based Attention Approach for Visual Tracking.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, May). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. Presented at 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

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, December). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. Presented at International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary

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.

Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery (2019)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Akcay, A., Guillen-Garcia, P., Barker, J., & Breckon, T. (2019, July). Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery. Presented at Proc. Int. Joint Conference on Neural Networks, Budapest, Hungary

X-ray baggage security screening is widely used to maintain aviation and transport secure. Of particular interestis the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items and liquids. How... Read More about Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery.