Outputs (5)

An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy (2017)
Presentation / Conference Contribution
Maciel-Pearson, B., & Breckon, T. (2017, December). An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy. Presented at The UK-RAS Network Conference on Robotics and Autonomous Systems: robots working for and among us., Bristol, England

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.

An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery (2017)
Presentation / Conference Contribution
Akcay, S., & Breckon, T. (2017, December). An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery. Presented at IEEE International Conference on Image Processing (ICIP), Beijing, China

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/s11042-017-5408-0

Temporal 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)
Presentation / Conference Contribution
Wu, R., Kamata, S., & Breckon, T. (2017, September). Face Recognition via Deep Sparse Graph Neural Networks. Presented at 28th British Machine Vision Conference (BMVC) 2017., London, UK

DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation (2017)
Presentation / Conference Contribution
Atapour-Abarghouei, A., & Breckon, T. (2017, September). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation. Presented at 28th British Machine Vision Conference (BMVC) 2017, London, UK

We address plausible hole filling in depth images in a computationally lightweight methodology that leverages recent advances in semantic scene segmentation. Firstly, we perform such segmentation over a co-registered color image, commonly available f... Read More about DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation.