no code implementations • 10 Nov 2023 • Jiaxu Liu, Zhengdi Yu, Toby P. Breckon, Hubert P. H. Shum
To achieve this, U3DS$^3$ leverages a generalized unsupervised segmentation method for both object and background across both indoor and outdoor static 3D point clouds with no requirement for model pre-training, by leveraging only the inherent information of the point cloud to achieve full 3D scene segmentation.
no code implementations • 25 Oct 2023 • Mehdi Rafiei, Toby P. Breckon, Alexandros Iosifidis
Anomaly detection methods have demonstrated remarkable success across various applications.
1 code implementation • ICCV 2023 • Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon, Hubert P. H. Shum, Chris G. Willcocks
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.
no code implementations • 1 May 2023 • Seyma Yucer, Furkan Tektas, Noura Al Moubayed, Toby P. Breckon
Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally.
1 code implementation • CVPR 2023 • Li Li, Hubert P. H. Shum, Toby P. Breckon
Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving.
Ranked #1 on 3D Semantic Segmentation on ScribbleKITTI (mIoU-1% metric)
1 code implementation • CVPR 2023 • Zhengdi Yu, Shaoli Huang, Chen Fang, Toby P. Breckon, Jue Wang
Our method significantly outperforms the best interacting-hand approaches on the InterHand2. 6M dataset while yielding comparable performance with the state-of-the-art single-hand methods on the FreiHand dataset.
Ranked #2 on 3D Interacting Hand Pose Estimation on InterHand2.6M
no code implementations • 24 Nov 2022 • Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
1 code implementation • CVPR 2023 • Brian K. S. Isaac-Medina, Chris G. Willcocks, Toby P. Breckon
In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one.
no code implementations • 29 Oct 2022 • Neelanjan Bhowmik, Toby P. Breckon
X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks.
no code implementations • 25 Oct 2022 • Qian Wang, Toby P. Breckon
Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptation task.
no code implementations • 16 Aug 2022 • Seyma Yucer, Matt Poyser, Noura Al Moubayed, Toby P. Breckon
Yes - This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject.
no code implementations • 1 Jun 2022 • William Prew, Toby P. Breckon, Magnus Bordewich, Ulrik Beierholm
We propose a continuous Gaussian representation of annotated grasps to generate ground truth training data which achieves a higher success rate on a simulated robotic grasping benchmark.
no code implementations • 16 May 2022 • Neelanjan Bhowmik, Jack W. Barker, Yona Falinie A. Gaus, Toby P. Breckon
When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0. 823 with Cascade R-CNN across the FLIR dataset, outperforming prior work.
1 code implementation • International Conference on 3D Vision (3DV) 2021 • Li Li, Khalid N. Ismail, Hubert P. H. Shum, Toby P. Breckon
Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased avail- ability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self- supervised loss formulation.
3 code implementations • 24 Nov 2021 • Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, Chris G. Willcocks
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.
Ranked #4 on Image Generation on LSUN Bedroom 256 x 256 (Recall metric)
no code implementations • 25 Oct 2021 • Qian Wang, Fanlin Meng, Toby P. Breckon
The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes.
1 code implementation • 19 Oct 2021 • Seyma Yucer, Furkan Tektas, Noura Al Moubayed, Toby P. Breckon
We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject.
no code implementations • 10 Oct 2021 • Thomas W. Webb, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond.
no code implementations • 27 Aug 2021 • Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
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.
no code implementations • 28 Apr 2021 • Jack W. Barker, Toby P. Breckon
Semi-supervised methods of anomaly detection have seen substantial advancement in recent years.
1 code implementation • 13 Apr 2021 • Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon, Hubert P. H. Shum
As unmanned aerial vehicles (UAVs) become more accessible with a growing range of applications, the potential risk of UAV disruption increases.
no code implementations • 12 Apr 2021 • Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain.
1 code implementation • 25 Mar 2021 • Brian K. S. Isaac-Medina, Matt Poyser, Daniel Organisciak, Chris G. Willcocks, Toby P. Breckon, Hubert P. H. Shum
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use.
no code implementations • 21 Dec 2020 • Qian Wang, Toby P. Breckon
Specifically, we formulate it as a 3D semantic segmentation problem to identify material types for all voxels based on which contraband materials can be detected.
no code implementations • 9 Dec 2020 • Naif Alshammari, Samet Akcay, Toby P. Breckon
For optimal performance in semantic segmentation, our model generates depth to be used as complementary source information with RGB in the segmentation network.
no code implementations • 9 Dec 2020 • Naif Alshammari, Samet Akcay, Toby P. Breckon
Using this architectural formulation with dense skip connections, our model achieves comparable performance to contemporary approaches at a fraction of the overall model complexity.
1 code implementation • 1 Dec 2020 • Qian Wang, Fanlin Meng, Toby P. Breckon
As a result, our proposed methods (i. e. naive-SPL and norm-VAE-SPL) can achieve new state-of-the-art performance with the average accuracy of 93. 4% and 90. 4% on Office-Caltech and ImageCLEF-DA datasets, and comparable performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97. 2%, 87. 6% and 67. 9% respectively.
2 code implementations • 17 Oct 2020 • William Thomson, Neelanjan Bhowmik, Toby P. Breckon
Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat).
no code implementations • 14 Aug 2020 • Toby P. Breckon, Aishah Alsehaim
Video-based person re-identification has received increasing attention recently, as it plays an important role within surveillance video analysis.
no code implementations • 3 Aug 2020 • Qian Wang, Toby P. Breckon
In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source domain samples as semantic representations for zero-shot learning.
no code implementations • 3 Aug 2020 • Qian Wang, Neelanjan Bhowmik, Toby P. Breckon
X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibited item detection focus primarily on 2D X-ray imagery.
no code implementations • 28 Jul 2020 • Matt Poyser, Amir Atapour-Abarghouei, Toby P. Breckon
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.
1 code implementation • 16 Jul 2020 • Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Toby P. Breckon
Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network.
Signal Processing Image and Video Processing
no code implementations • 5 May 2020 • Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches.
1 code implementation • 26 Apr 2020 • Qian Wang, Toby P. Breckon
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).
no code implementations • 19 Apr 2020 • Seyma Yucer, Samet Akçay, Noura Al-Moubayed, Toby P. Breckon
Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society.
no code implementations • 27 Mar 2020 • Qian Wang, Neelanjan Bhowmik, Toby P. Breckon
As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features.
1 code implementation • 15 Jan 2020 • Qian Wang, Najla Megherbi, Toby P. Breckon
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.
no code implementations • 11 Dec 2019 • Bruna G. Maciel-Pearson, Letizia Marchegiani, Samet Akcay, Amir Atapour-Abarghouei, James Garforth, Toby P. Breckon
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet unsolved challenge.
no code implementations • 20 Nov 2019 • Ganesh Samarth C. A., Neelanjan Bhowmik, Toby P. Breckon
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.
no code implementations • 20 Nov 2019 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay, Toby P. Breckon
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items.
no code implementations • 19 Nov 2019 • Neelanjan Bhowmik, Yona Falinie A. Gaus, Samet Akcay, Jack W. Barker, Toby P. Breckon
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles.
1 code implementation • 18 Nov 2019 • Qian Wang, Toby P. Breckon
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.
Ranked #3 on Crowd Counting on ShanghaiTech B
1 code implementation • 18 Nov 2019 • Qian Wang, Toby P. Breckon
Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains.
Ranked #1 on Domain Adaptation on Office-Caltech
no code implementations • 25 Sep 2019 • Neelanjan Bhowmik, Qian Wang, Yona Falinie A. Gaus, Marcin Szarek, Toby P. Breckon
This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.
no code implementations • 17 Sep 2019 • Naif Alshammari, Samet Akçay, Toby P. Breckon
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e. g. foggy weather), which results in poor outdoor scene visibility.
no code implementations • 15 Aug 2019 • Amir Atapour-Abarghouei, Toby P. Breckon
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.
2 code implementations • 18 Jul 2019 • Bruna G. Maciel-Pearson, Samet Akcay, Amir Atapour-Abarghouei, Christopher Holder, Toby P. Breckon
Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications.
Ranked #1 on Autonomous Flight (Dense Forest) on mtrl-auto-uav
1 code implementation • 28 Apr 2019 • Fady Medhat, Mahnaz Mohammadi, Sardar Jaf, Chris G. Willcocks, Toby P. Breckon, Peter Matthews, Andrew Stephen McGough, Georgios Theodoropoulos, Boguslaw Obara
In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance.
no code implementations • 10 Apr 2019 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign).
1 code implementation • 26 Mar 2019 • Amir Atapour-Abarghouei, Toby P. Breckon
Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation.
Ranked #67 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • 25 Mar 2019 • Qian Wang, Penghui Bu, Toby P. Breckon
Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain.
Ranked #3 on Domain Adaptation on Office-Caltech
no code implementations • 25 Mar 2019 • Qian Wang, Khalid N. Ismail, Toby P. Breckon
To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.
2 code implementations • 25 Jan 2019 • Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.
1 code implementation • 20 Nov 2018 • Qian Wang, Ning Jia, Toby P. Breckon
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.
no code implementations • ECCV 2018 • Greire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Recent automotive vision work has focused almost exclusively on processing forward-facing cameras.
1 code implementation • ECCV 2018 • Grégoire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Recent automotive vision work has focused almost exclusively on processing forward-facing cameras.
1 code implementation • CVPR 2018 • Amir Atapour-Abarghouei, Toby P. Breckon
Monocular depth estimation using learning-based approaches has become promising in recent years.
9 code implementations • 17 May 2018 • Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
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).
Generative Adversarial Network Semi-supervised Anomaly Detection +1
no code implementations • 4 Jan 2018 • Christopher J. Holder, Toby P. Breckon, Xiong Wei
Scene understanding for autonomous vehicles is a challenging computer vision task, with recent advances in convolutional neural networks (CNNs) achieving results that notably surpass prior traditional feature driven approaches.