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U3DS3 : Unsupervised 3D Semantic Scene Segmentation (2024)
Conference Proceeding
Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024). U3DS3 : Unsupervised 3D Semantic Scene Segmentation. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (3747-3756). https://doi.org/10.1109/WACV57701.2024.00372

Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However , it is both time-consuming and challenging to obtain consistently accurate annotations for such 3D scene data. Moreover, there is still a lac... Read More about U3DS3 : Unsupervised 3D Semantic Scene Segmentation.

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. (2024). 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.

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation (2024)
Journal Article
Wang, Q., Meng, F., & Breckon, T. P. (2024). Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/TAI.2024.3379940

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set... Read More about Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation.

Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers (2023)
Conference Proceeding
Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. In ICCV '23: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. https://doi.org/10.1109/ICCV51070.2023.01341

Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks... Read More about Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Neural architecture search: A contemporary literature review for computer vision applications (2023)
Journal Article
Poyser, M., & Breckon, T. P. (2024). Neural architecture search: A contemporary literature review for computer vision applications. Pattern Recognition, 147, 110052. https://doi.org/10.1016/j.patcog.2023.110052

Deep Neural Networks have received considerable attention in recent years. As the complexity of network architecture increases in relation to the task complexity, it becomes harder to manually craft an optimal neural network architecture and train it... Read More about Neural architecture search: A contemporary literature review for computer vision applications.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening (2023)
Presentation / Conference Contribution

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection perfor... Read More about Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening.

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

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.

Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields (2023)
Presentation / Conference Contribution

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may res... Read More about Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.

Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery (2022)
Presentation / Conference Contribution

X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items,... Read More about Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery.

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes (2022)
Presentation / Conference Contribution

Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements. Recent Vector-Quantized image models have... Read More about Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes.

Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery (2022)
Presentation / Conference Contribution

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression c... Read More about Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery.

Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery (2022)
Presentation / Conference Contribution

Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades a... Read More about Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery.

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications (2021)
Presentation / Conference Contribution

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq... Read More about DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications.

Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition (2021)
Presentation / Conference Contribution

We uniquely consider the task of joint person re-identification (Re-ID) and action recognition in video as a multi-task problem. In addition to the broader potential of joint Re-ID and action recognition within the context of automated multi-camera s... Read More about Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Presentation / Conference Contribution

Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems. Common technologies for... Read More about Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.

On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening (2021)
Presentation / Conference Contribution

We address the automatic contraband material detection problem within volumetric 3D Computed Tomography (CT) data for baggage security screening. Distinct from the prohibited item detection using object detection techniques, contraband material detec... Read More about On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening.

On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks (2021)
Presentation / Conference Contribution

Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb) X-ray imagery.... Read More about On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.

Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging (2021)
Journal Article

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

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.

Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation (2021)
Presentation / Conference Contribution

— Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are app... Read More about Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation.

Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation (2021)
Presentation / Conference Contribution

Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather),... Read More about Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation.

Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery (2021)
Presentation / Conference Contribution

Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limit... Read More about Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery.

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

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.

Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery (2021)
Presentation / Conference Contribution

Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the vie... Read More about Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (2021)
Presentation / Conference Contribution

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bo... Read More about Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection.

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

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.

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

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.

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

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.

Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items (2019)
Presentation / Conference Contribution

X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of part... Read More about Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items.

Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery (2019)
Presentation / Conference Contribution

X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives wit... Read More about Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray 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

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

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.

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (2019)
Presentation / Conference Contribution

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced co... Read More about Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection.

An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening (2019)
Journal Article

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.

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

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.

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.

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

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

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.

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

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

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.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution

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

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.

Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery (2019)
Presentation / Conference Contribution

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.

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

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.

On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery (2019)
Journal Article

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

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

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.

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

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.

On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding (2018)
Presentation / Conference Contribution

Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms a... Read More about On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding.

Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection (2018)
Presentation / Conference Contribution

In this work we investigate the automatic detection of fire pixel regions in video (or still) imagery within real-time bounds without reliance on temporal scene information. As an extension to prior work in the field, we consider the performance of e... Read More about Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection.

Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery (2018)
Presentation / Conference Contribution

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360... Read More about Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery.

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Presentation / Conference Contribution

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.

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery (2018)
Journal Article

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

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.

An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy (2017)
Presentation / Conference Contribution

Autonomous flight within a forest canopy represents a key challenge for generalised scene understanding on-board a future Unmanned Aerial Vehicle (UAV) platform. Here we present an approach for automatic trail navigation within such an environment th... Read More about An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy.

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

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.

From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes (2016)
Presentation / Conference Contribution

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN arc... Read More about From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes.

Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery (2016)
Presentation / Conference Contribution

We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, tradition... Read More about Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery.

Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow (2016)
Presentation / Conference Contribution

Mapping an ever changing urban environment is a challenging task as we are generally interested in mapping the static scene and not the dynamic objects, such as cars and people. We propose a novel approach to the problem of dynamic object removal wit... Read More about Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow.

Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications (2016)
Presentation / Conference Contribution

We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computati... Read More about Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications.

Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR) (2016)
Presentation / Conference Contribution

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. An... Read More about Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR).

On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening (2016)
Presentation / Conference Contribution

Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage security X-ray imagery. Using a classical Bo... Read More about On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening.

Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications (2015)
Journal Article
Kriechbaumer, T., Blackburn, K., Breckon, T., Hamilton, O., & Riva-Casado, M. (2015). Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications. Sensors, 15(12), 31869-31887. https://doi.org/10.3390/s151229892

Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetat... Read More about Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications.

Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery (2015)
Journal Article
Chermak, L., Breckon, T., Flitton, G., & Megherbi, N. (2015). Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery. The Imaging Science Journal, https://doi.org/10.1179/1743131x15y.0000000019

This study presents a novel method for liquid detection within three-dimensional (3D) computed tomography (CT) baggage inspection imagery. Liquid detection within airport security is currently of significant interest due to security threats associate... Read More about Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery.

Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation (2015)
Presentation / Conference Contribution

The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly import contexts of visual surveillance and vehicle aut... Read More about Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation.

Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery (2015)
Presentation / Conference Contribution

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with ac... Read More about Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery.

Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots (2015)
Presentation / Conference Contribution

We consider the use of low-budget omnidirectional platforms for 3D mapping and self-localisation. These robots specifically permit rotational motion in the plane around a central axis, with negligible displacement. In addition, low resolution and com... Read More about Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots.

A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening (2015)
Journal Article
Mouton, A., & Breckon, T. (2015). A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 23(5), 531-555. https://doi.org/10.3233/xst-150508

Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security r... Read More about A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening.

Robust visual tracking via speedup multiple kernel ridge regression (2015)
Journal Article
Qian, C., Breckon, T. P., & Li, H. (2015). Robust visual tracking via speedup multiple kernel ridge regression. Journal of Electronic Imaging, 24(5), Article 053016. https://doi.org/10.1117/1.jei.24.5.053016

Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize... Read More about Robust visual tracking via speedup multiple kernel ridge regression.

Real-time construction and visualisation of drift-free video mosaics from unconstrained camera motion (2015)
Journal Article
Breszcz, M., & Breckon, T. (2015). Real-time construction and visualisation of drift-free video mosaics from unconstrained camera motion. Journal of Engineering, 2015(8), 229-240. https://doi.org/10.1049/joe.2015.0016

This work proposes a novel approach for real-time video mosaicking facilitating drift-free mosaic construction and visualisation, with integrated frame blending and redundancy management, that is shown to be flexible to a range of varying mosaic scen... Read More about Real-time construction and visualisation of drift-free video mosaics from unconstrained camera motion.

Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks (2015)
Journal Article
Flitton, G., Mouton, A., & Breckon, T. (2015). Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks. Pattern Recognition, 48(8), 2489-2499. https://doi.org/10.1016/j.patcog.2015.02.006

We investigate the performance of a Bag of (Visual) Words (BoW) object classification model as an approach for automated threat object detection within 3D Computed Tomography (CT) imagery from a baggage security context. This poses a novel and unique... Read More about Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks.

Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening (2015)
Journal Article
Mouton, A., & Breckon, T. (2015). Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening. Pattern Recognition, 48(6), 1961-1978. https://doi.org/10.1016/j.patcog.2015.01.010

We present a novel technique for the 3D segmentation of unknown objects from cluttered dual-energy Computed Tomography (CT) data obtained in the baggage security-screening domain. Initial materials-based coarse segmentations, generated using the Dual... Read More about Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening.

A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions (2014)
Presentation / Conference Contribution

Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination condi... Read More about A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions.

3D Object Classification in Baggage Computed Tomography Imagery using Randomised Clustering Forests (2014)
Presentation / Conference Contribution

We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of... Read More about 3D Object Classification in Baggage Computed Tomography Imagery using Randomised Clustering Forests.

A Photogrammetric Approach for Real-time 3D Localization and Tracking of Pedestrians in Monocular Infrared Imagery (2014)
Presentation / Conference Contribution

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with ac... Read More about A Photogrammetric Approach for Real-time 3D Localization and Tracking of Pedestrians in Monocular Infrared Imagery.

Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo (2014)
Presentation / Conference Contribution

We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the... Read More about Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo.

A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery (2013)
Journal Article
Flitton, G., Breckon, T., & Megherbi, N. (2013). A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery. Pattern Recognition, 46(9), 2420-2436. https://doi.org/10.1016/j.patcog.2013.02.008

We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region hi... Read More about A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery.

An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment (2012)
Journal Article
Mroz, F., & Breckon, T. (2012). An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment. EURASIP Journal on Image and Video Processing, 2012, Article 13. https://doi.org/10.1186/1687-5281-2012-13

In this work we evaluate the use of several real-time dense stereo algorithms as a passive 3D sensing technology for potential use as part of a driver assistance system or autonomous vehicle guidance. A key limitation in prior work in this area is th... Read More about An Empirical Comparison of Real-time Dense Stereo Approaches for use in the Automotive Environment.