Publications (8)
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation
Felix Stephenson, Toby Breckon, Ioannis Katramados
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 d…
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
Noura Al Moubayed, Toby Breckon, Peter Matthews +1
In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a…
Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner +3
Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals i…
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss
William Prew, Toby Breckon, Magnus Bordewich +1
In this paper, we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition…
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
Bing Wang, Zhengdi Yu, Bo Yang +5
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields…
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
Samet Akcay, Toby Breckon
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…
Style Augmentation: Data Augmentation via Style Randomization
Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner +2
We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both class…
Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
Xin Zhang, Qian Wang, Toby Breckon +1
We present a method for reading digital data embedded in planar 3D printed surfaces. The data are organised in binary arrays and embedded as surface textures in a way inspired by Q…