All Outputs (11)

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). Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery. In Proc. Int. Conf. on Pattern Recognition (2813-2818). https://doi.org/10.1109/ICPR.2016.7900062

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). Real-time Classification of Vehicle Types within Infra-red Imagery. In D. Burgess, F. Carlysle-Davies, G. Owen, H. Bouma, R. Stokes, & Y. Yitzhaky (Eds.), Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-16). https://doi.org/10.1117/12.2241106

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.

From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes (2016)
Presentation / Conference Contribution
Holder, C., Breckon, T., & Wei, X. (2016). From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes. In G. Hua, & H. Jégou (Eds.), Computer Vision – ECCV 2016 workshops : Amsterdam, The Netherlands, October 8-10 and 15-16, 2016. Proceedings. Part I (149-162). https://doi.org/10.1007/978-3-319-46604-0_11

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN arc... Read More about From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes.

Constant-time Bilateral Filter using Spectral Decomposition (2016)
Presentation / Conference Contribution
Sugimoto, K., Breckon, T., & Kamata, S. (2016). Constant-time Bilateral Filter using Spectral Decomposition. In Proc. Int. Conf. on Image Processing (3319-3323). https://doi.org/10.1109/ICIP.2016.7532974

This paper presents an efficient constant-time bilateral filter where constant-time means that computational complexity is independent of filter window size. Many state-of-the-art constant-time methods approximate the original bilateral filter by an... Read More about Constant-time Bilateral Filter using Spectral Decomposition.

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). Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery. In Proc. Int. Conf. on Image Processing (1057 -1061). https://doi.org/10.1109/ICIP.2016.7532519

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). Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow. In Proc. Int. Conf. on Image Processing (3439-3443). https://doi.org/10.1109/ICIP.2016.7532998

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). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. In Proc. Int. Conf. on Image Processing (300-304). https://doi.org/10.1109/ICIP.2016.7532367

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). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II (423-430). https://doi.org/10.1007/978-3-319-44781-0_50

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., …Styles, T. (2016). Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR). In M. A. Kolodny, & T. Pham (Eds.), Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII (1-18). https://doi.org/10.1117/12.2229720

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). On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening. In Proc. Int. Conf. on Imaging for Crime Detection and Prevention (12 (6 .)-12 (6 .)(1)). https://doi.org/10.1049/ic.2016.0080

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.