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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.

Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery (2020)
Presentation / Conference Contribution
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

Object detection from infrared-band (thermal) imagery has been a challenging problem for many years. With the advent of deep Convolutional Neural Networks (CNN), the automated detection and classification of objects of interest within the scene has b... Read More about Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery.

A Ranking based Attention Approach for Visual Tracking (2019)
Presentation / Conference Contribution
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

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.

Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding (2019)
Presentation / Conference Contribution
Huang, Y., Long, Y., & Wang, L. (2019). Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding. In Thirty-Second AAAI Conference on Artificial Intelligence ; proceedings (5342-5349)

Word similarity and word relatedness are fundamental to natural language processing and more generally, understanding how humans relate concepts in semantic memory. A growing number of datasets are being proposed as evaluation benchmarks,however, the... Read More about Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding.

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.

Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction (2019)
Presentation / Conference Contribution
Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. In I. V. Tetko, V. Kůrková, P. Karpov, & F. Theis (Eds.), Artificial neural networks and machine learning – ICANN 2019; 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019 ; proceedings (338-350). https://doi.org/10.1007/978-3-030-30493-5_34

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Ty... Read More about Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.

On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2020)
Presentation / Conference Contribution
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

X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of exte... Read More about On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation (2019)
Presentation / Conference Contribution
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

Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulti... Read More about DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation.

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks (2019)
Presentation / Conference Contribution
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

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the b... Read More about A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks.

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery (2019)
Presentation / Conference Contribution
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

X-ray imagery security screening is essential to maintaining transport security against a varying profile of prohibited items. Particular interest lies in the automatic detection and classification of prohibited items such as firearms and firearm com... Read More about On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery.

On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery (2019)
Presentation / Conference Contribution
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

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anom... Read More about On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery.

On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding (2019)
Presentation / Conference Contribution
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

Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles)... Read More about On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding.