Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy
(2018)
Conference Proceeding
Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. In Proc. Towards Autonomous Robotic Systems Conference (147-158). https://doi.org/10.1007/978-3-319-96728-8_13
Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction (2018)
Conference Proceeding
Holder, C., & Breckon, T. (2018). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction. In Proc. Intelligent Vehicles Symposium (2104-2110). https://doi.org/10.1109/IVS.2018.8500526
On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Conference Proceeding
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.
Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer (2018)
Conference Proceeding
Atapour-Abarghouei, A., & Breckon, T. (2018). Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer. In Proc. Computer Vision and Pattern Recognition (2800-2810). https://doi.org/10.1109/CVPR.2018.00296Monocular 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.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018)
Conference Proceeding
Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. In C. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision – ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III (622-637). https://doi.org/10.1007/978-3-030-20893-6_39Anomaly 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). While... Read More about GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.
On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks (2018)
Conference Proceeding
Guo, T., Akcay, S., Adey, P., & Breckon, T. (2018). On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks. In Proc. Int. Conf. on Image Processing (3413-3417). https://doi.org/10.1109/ICIP.2018.8451453
Infrared Image Colorization Using S-Shape Network (2018)
Conference Proceeding
Dong, Z., Kamata, S., & Breckon, T. (2018). Infrared Image Colorization Using S-Shape Network. In Proc. Int. Conf. on Image Processing (2242-2246). https://doi.org/10.1109/ICIP.2018.8451230This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-dec... Read More about Infrared Image Colorization Using S-Shape Network.
Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection (2018)
Conference Proceeding
Dunnings, A., & Breckon, T. (2018). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. In Proc. Int. Conf. on Image Processing (1558-1562). https://doi.org/10.1109/ICIP.2018.8451657In 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)
Conference Proceeding
Payen de La Garanderie, G., Atapour-Abarghouei, A., & Breckon, T. (2018). Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery.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.
On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling (2018)
Conference Proceeding
Loveday, M., & Breckon, T. (2018). On the Impact of Parallax Free Colour and Infrared Image Co-Registration to Fused Illumination Invariant Adaptive Background Modelling. In Proc. Computer Vision and Pattern Recognition Workshops (1267-1276). https://doi.org/10.1109/CVPRW.2018.00164
Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras (2018)
Conference Proceeding
Lin, K., & Breckon, T. (2018). Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras. In Proc. Int. Conf. Image Analysis and Recognition (315-325). https://doi.org/10.1007/978-3-319-93000-8_36With the rise of consumer-grade spherical cameras, offering full omni-directional 360∘ image capture, the potential for low-cost omni-directional stereo vision is ever present. Whilst this potentially offers novel low-cost omni-directional depth sens... Read More about Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras.
Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments (2018)
Conference Proceeding
Holder, C., & Breckon, T. (2018). Encoding Stereoscopic Depth Features for Scene Understanding in Off-Road Environments. In Proc. Int. Conf. Image Analysis and Recognition (427-434). https://doi.org/10.1007/978-3-319-93000-8_48
Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Conference Proceeding
Atapour-Abarghouei, A., & Breckon, T. P. (2018). Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion. In A. Campilho, F. Karray, & B. T. H. Romeny (Eds.), Image analysis and recognition : 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018 ; proceedings (306-314). https://doi.org/10.1007/978-3-319-93000-8_35We 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.
On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding (2018)
Conference Proceeding
Alshammari, N., Akcay, S., & Breckon, T. (2018). On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding.
Extended Patch Prioritization For Depth Hole Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Conference Proceeding
Atapour-Abarghouei, A., & Breckon, T. (2018). Extended Patch Prioritization For Depth Hole 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
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.2018.2812196We 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.2018.02.001Despite 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)
Conference Proceeding
Maciel-Pearson, B., & Breckon, T. (2017). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. In Proc. Conf. on Robotics and Autonomous Systems - Robots that Work Among Us Workshop (1-3)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.
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/s11042-017-5408-0Temporal 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.
Face Recognition via Deep Sparse Graph Neural Networks (2017)
Conference Proceeding
Wu, R., Kamata, S., & Breckon, T. (2017). Face Recognition via Deep Sparse Graph Neural Networks. In Proc. British Machine Vision Conference Workshops (1-8)
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