Outputs (190)

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders (2023)
Journal Article
Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classific... Read More about Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders.

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation (2023)
Journal Article
Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified c... Read More about Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation.

Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss (2022)
Journal Article
Wang, Q., & Breckon, T. (online). Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/tits.2021.3138896

Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants. Crowd counting is one of the keys to automatic crowd behaviour a... Read More about Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss.

Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation (2021)
Journal Article
Wang, Q., & Breckon, T. (2022). Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation. Pattern Recognition, 123, Article 108362. https://doi.org/10.1016/j.patcog.2021.108362

Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e.g., texts and images) or feature dimensions (e.g., features extracted with different methods).... Read More about Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation.

Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging (2021)
Journal Article
Akcay, S., & Breckon, T. (2022). Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging. Pattern Recognition, 122, Article 108245. https://doi.org/10.1016/j.patcog.2021.108245

X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising... Read More about Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging.

Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video (2021)
Journal Article
Gökstorp, S., & Breckon, T. (2022). Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video. Visual Computer, 38(6), 2033-2040. https://doi.org/10.1007/s00371-021-02264-6

Unmanned Aerial Vehicles (UAV) can be used to great effect for wide-area searches such as search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose inv... Read More about Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video.

Domain Adaptation via Image Style Transfer (2020)
Book Chapter
Atapour-Abarghouei, A., & Breckon, T. (2020). Domain Adaptation via Image Style Transfer. In H. Venkateswara, & S. Panchanathan (Eds.), Domain adaptation in computer vision with deep learning (137-156). Springer Verlag. https://doi.org/10.1007/978-3-030-45529-3_8

While recent growth in modern machine learning techniques has led to remarkable strides in computer vision applications, one of the most significant challenges facing learning-based vision systems is the scarcity of large, high-fidelity datasets requ... Read More about Domain Adaptation via Image Style Transfer.

A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes (2020)
Journal Article
Wang, Q., Megherbi, N., & Breckon, T. (2020). A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 28(3), 507-526. https://doi.org/10.3233/xst-200654

BACKGROUND: Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner. It has been shown to be highl... Read More about A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes.

An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening (2019)
Journal Article
Wang, Q., Ismail, K., & Breckon, T. (2020). An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 28(1), 35-58. https://doi.org/10.3233/xst-190531

BACKGROUND: The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviati... Read More about An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening.

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.

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.

Discrete Curvature Representations for Noise Robust Image Corner Detection (2019)
Journal Article
Zhang, W., Sun, C., Breckon, T., & Alshammari, N. (2019). Discrete Curvature Representations for Noise Robust Image Corner Detection. IEEE Transactions on Image Processing, 28(9), 4444-4459. https://doi.org/10.1109/tip.2019.2910655

Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local v... Read More about Discrete Curvature Representations for Noise Robust Image Corner Detection.

Learning to Drive: End-to-End Off-Road Path Prediction (2019)
Journal Article
Holder, C., & Breckon, T. (2021). Learning to Drive: End-to-End Off-Road Path Prediction. IEEE Intelligent Transportation Systems Magazine, 13(2), 217-221. https://doi.org/10.1109/mits.2019.2898970

Autonomous driving is a field currently gaining a lot of attention, and recently ?end to end? approaches, whereby a machine learning algorithm learns to drive by emulating a human driver, have demonstrated significant potential. However, recent work... Read More about Learning to Drive: End-to-End Off-Road Path Prediction.

On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery (2019)
Journal Article
Mouton, A., & Breckon, T. (2019). On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 27(1), 51-72. https://doi.org/10.3233/xst-180411

We evaluate the impact of denoising and Metal Artefact Reduction (MAR) on 3D object segmentation and classification in low-resolution, cluttered dual-energy Computed Tomography (CT). To this end, we present a novel 3D materials-based segmentation tec... Read More about On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery.

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications (2019)
Journal Article
Podmore, J., Breckon, T., Aznan, N., & Connolly, J. (2019). On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 611-618. https://doi.org/10.1109/tnsre.2019.2904791

Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alp... Read More about On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications.

Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer (2019)
Journal Article
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

In this work, the issue of depth filling is addressed using a self-supervised feature learning model that predicts missing depth pixel values based on the context and structure of the scene. A fully-convolutional generative model is conditioned on th... Read More about Generative Adversarial Framework for Depth Filling via Wasserstein Metric, Cosine Transform and Domain Transfer.

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery (2018)
Journal Article
Akcay, S., Kundegorski, M., Willcocks, C., & Breckon, T. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery. IEEE Transactions on Information Forensics and Security, 13(9), 2203-2215. https://doi.org/10.1109/tifs.2018.2812196

We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts... Read More about Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery.

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.

Clustering in pursuit of temporal correlation for human motion segmentation (2017)
Journal Article
Qian, C., Breckon, T., & Xu, Z. (2018). Clustering in pursuit of temporal correlation for human motion segmentation. Multimedia Tools and Applications, 77(15), 19615-19631. https://doi.org/10.1007/s11042-017-5408-0

Temporal correlation is an important property of the video sequence. However, most methods only accomplish the clustering of frames via the measurement of similarity between frame pair, and the temporal correlation among frames is rarely taken into a... Read More about Clustering in pursuit of temporal correlation for human motion segmentation.

Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels (2016)
Journal Article
Zhang, W., Zhao, Y., Breckon, T., & Chen, L. (2016). Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels. Pattern Recognition, 63(8), 193-205. https://doi.org/10.1016/j.patcog.2016.10.008

This paper presents a novel noise robust edge detector based upon the automatic anisotropic Gaussian kernels (ANGKs), which also addresses the current problem that the seminal Canny edge detector may miss some obvious crossing edge details. Firstly,... Read More about Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels.