Outputs (204)

Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery (2016)
Presentation / Conference Contribution
Akcay, S., Kundegorski, M., Devereux, M., & Breckon, T. (2016, September). Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA

We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, tradition... Read More about Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery.

Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow (2016)
Presentation / Conference Contribution
Hamilton, O., & Breckon, T. (2016, September). Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA

Mapping an ever changing urban environment is a challenging task as we are generally interested in mapping the static scene and not the dynamic objects, such as cars and people. We propose a novel approach to the problem of dynamic object removal wit... Read More about Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow.

Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications (2016)
Presentation / Conference Contribution
Katramados, I., & Breckon, T. (2016, September). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA

We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computati... Read More about Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications.

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder (2016)
Presentation / Conference Contribution
Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

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 small set of labelled data samples. Features are extracted using topi... Read More about SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder.

Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR) (2016)
Presentation / Conference Contribution
Thomas, P., Marshall, G., Faulkner, D., Kent, P., Page, S., Islip, S., Oldfield, J., Breckon, T., Kundegorski, M., Clarke, D., & Styles, T. (2016, April). Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR). Presented at SPIE Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent Intelligence Surveillance and Reconnaissance VII, Baltimore, Maryland, USA

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. An... Read More about Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR).

On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening (2016)
Presentation / Conference Contribution
Kundegorski, M., Akcay, S., Devereux, M., Mouton, A., & Breckon, T. (2016, January). On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening. Presented at International Conference on Imaging for Crime Detection and Prevention, Madrid, Spain

Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage security X-ray imagery. Using a classical Bo... Read More about On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening.

Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications (2015)
Journal Article
Kriechbaumer, T., Blackburn, K., Breckon, T., Hamilton, O., & Riva-Casado, M. (2015). Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications. Sensors, 15(12), 31869-31887. https://doi.org/10.3390/s151229892

Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetat... Read More about Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications.

Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation (2015)
Presentation / Conference Contribution
Webster, D., & Breckon, T. (2015, September). Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation. Presented at Proceedings of IEEE International Conference on Image Processing, Québec City, Canada

The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly import contexts of visual surveillance and vehicle aut... Read More about Improved Raindrop Detection using Combined Shape and Saliency Descriptors with Scene Context Isolation.

Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery (2015)
Presentation / Conference Contribution
Kundegorski, M., & Breckon, T. (2015, September). Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery. Presented at Optics and Photonics for Counterterrorism, Crime Fighting and Defence, Toulouse, France

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with ac... Read More about Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery.

Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots (2015)
Presentation / Conference Contribution
Cavestany, P., Rodríguez, A., Rodriguez, A., Martínez-Barberá, H., Martinez-Barbera, H., & Breckon, T. (2015, September). Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots. Presented at Proceedings of IEEE International Conference on Image Processing, Québec City, Canada

We consider the use of low-budget omnidirectional platforms for 3D mapping and self-localisation. These robots specifically permit rotational motion in the plane around a central axis, with negligible displacement. In addition, low resolution and com... Read More about Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots.