Durham University, UK
St Chad's College, Durham.
Addressing key research questions relating to vehicle autonomy in terms of "Where am I ?" (localisation) and "What is around me?" (scene understanding) in terms of both scene geometry (3D depth ) and semantics (object detection & classification).
Addressing the eternal security screening challenge of "What's in the bag?" via object detection, classification and segmentation within complex, cluttered X-ray security scanner imagery.
Addressing real-time image understanding challenges relating to "Is there anything there?" (detection), "What is it?" (classification), "Where is it?" (localization) and "What is it’s behaviour?" (tracking) across both fixed and mobile (robotic) sensor networks.
Addressing the classical and overarching challenge of enabling robots of all shapes and sizes to have perception of their environment in the same way as humans do - to know "What is where by looking?".
A selection of representative works ...
2019 | ||
| [aznan19navigation] |  | Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation , In Proc. Int. Conf. Robotics and Automation, IEEE, pp. 4889-4895, 2019.Keywords: ssvep, brain computer interface, bci, cnn, neural networks, convolutional neural networks, deep learning, dry-eeg, robot guidance. |
2018 | ||
| [akcay18architectures] |  | On Using Deep Convolutional Neural Network Architectures for Automated Object Detection and Classification within X-ray Baggage Security Imagery , In IEEE Transactions on Information Forensics & Security, IEEE, Volume 13, No. 9, pp. 2203-2215, 2018.Keywords: x-ray security screening, automatic threat detection, firearms detection, airport security, deep learning, region-based convolutional neural networks, CNN, R-CNN, R-FCN, RCNN, Faster RCNN, ResNet, YOLO. |
| [akcay18ganomaly] |  | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , In Proc. Asian Conference on Computer Vision, Springer, pp. 622-637, 2018.Keywords: anomaly detection, GAN, generative adversarial networks. |
| [abarghouei18monocular] |  | Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer , In Proc. Computer Vision and Pattern Recognition, IEEE/CVF, pp. 2800-2810, 2018.Keywords: monocular depth, generative adversarial network, GAN, depth map, disparity, depth from single image, style transfer. |
2013 | ||
| [breckon13autonomous] |  | Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance , In Proc. SPIE Emerging Technologies in Security and Defence: Unmanned Sensor Systems, SPIE, Volume 8899, No. 01, pp. 1-19, 2013.Keywords: autonomous robots, grand challenge, wide area search, search and rescue, UAV, infrared, thermal. |
2012 | ||
| [flitton12cortex] |  | A 3D Extension to Cortex Like Mechanisms for 3D Object Class Recognition , In Proc. Computer Vision and Pattern Recognition, IEEE, pp. 3634-3641, 2012.Keywords: hmax, automatic baggage screening, 3D object recognition, 3D baggage classification, CT baggage scanning, threat detection in baggage. |
2008 | ||
| [breckon08completion] |  | 3D Surface Relief Completion Via Non-parametric Techniques , In IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, Volume 30, No. 12, pp. 2249 - 2255, 2008.Keywords: 3D relief completion, amodal completion, volume completion, visual completion, perceptual completion, visual propagation, 3D surface completion. |
