114 citations · 579 across the 79 of their papers we have counts for
8 papers · 1 filter
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…
Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation
Nik Khadijah Nik Aznan, Jason D. Connolly, Noura Al Moubayed +1
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 Netwo…
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
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 p…
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…
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
Grégoire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensi…
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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…
On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks
Nik Khadijah Nik Aznan, Stephen Bonner, Jason D. Connolly +2
In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effec…
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
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 surpa…