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All Outputs (207)

Infrared Image Colorization Using S-Shape Network (2018)
Presentation / Conference Contribution
Dong, Z., Kamata, S., & Breckon, T. (2018, October). Infrared Image Colorization Using S-Shape Network. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece

This 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)
Presentation / Conference Contribution
Dunnings, A., & Breckon, T. (2018, October). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece

In 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)
Presentation / Conference Contribution
Payen de La Garanderie, G., Atapour-Abarghouei, A., & Breckon, T. (2018, September). Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. Presented at 15th European Conference on Computer Vision (ECCV 2018), Munich, Germany

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.

Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras (2018)
Presentation / Conference Contribution
Lin, K., & Breckon, T. (2018, June). Real-time Low-Cost Omni-directional Stereo Vision via Bi-Polar Spherical Cameras. Presented at 15th International Conference on Image Analysis and Recognition (ICIAR 2018)., Póvoa de Varzim, Portugal

With 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.

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Presentation / Conference Contribution
Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion. Presented at International Conference Image Analysis and Recognition, Póvoa de Varzim, Portugal

We 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.

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.2018.2812196

We 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.2018.02.001

Despite 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.

Author profiling: Gender prediction from Tweets and images: Notebook for PAN at CLEF 2018 (2018)
Presentation / Conference Contribution
HaCohen-Kerner, Y., Yigal, Y., Shayovitz, E., Miller, D., & Breckon, T. (2018, October). Author profiling: Gender prediction from Tweets and images: Notebook for PAN at CLEF 2018. Presented at CEUR Workshop Proceedings, Torino, Italy

Author profiling deals with identification of various details about the author of the text (e.g., age, cultural background, gender, native language, personality). In this paper, we describe the participation of our teams (yigall8 and millerl8, both t... Read More about Author profiling: Gender prediction from Tweets and images: Notebook for PAN at CLEF 2018.

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.

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.

Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery (2016)
Presentation / Conference Contribution
Atapour-Abarghouei, A., de La Garanderie, G. P., & Breckon, T. P. (2016, December). Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery. Presented at 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun

We address the problem of hole filling in RGB-D (color and depth) images, obtained from either active or stereo based sensing, for the purposes of object removal and missing depth estimation. This is performed independently on the low frequency depth... Read More about Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery.

Real-time Classification of Vehicle Types within Infra-red Imagery (2016)
Presentation / Conference Contribution
Kundegorski, M., Akcay, S., Payen de La Garanderie, G., Breckon, T., & Stokes, R. (2016, November). Real-time Classification of Vehicle Types within Infra-red Imagery. Presented at Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII, Edinburgh, United Kingdom

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration... Read More about Real-time Classification of Vehicle Types within Infra-red Imagery.

Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels (2016)
Journal Article
Zhang, W., Zhao, Y., Breckon, T., & Chen, L. (2016). Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels. Pattern Recognition, 63(8), 193-205. https://doi.org/10.1016/j.patcog.2016.10.008

This paper presents a novel noise robust edge detector based upon the automatic anisotropic Gaussian kernels (ANGKs), which also addresses the current problem that the seminal Canny edge detector may miss some obvious crossing edge details. Firstly,... Read More about Noise Robust Image Edge Detection based upon the Automatic Anisotropic Gaussian Kernels.