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Outputs (663)

Varifocal virtuality: a novel optical layout for near-eye display (2017)
Presentation / Conference Contribution
Aksit, K., Ng, R., Banks, M. S., Love, G. D., Lopes, W., Kim, J., …Srinivasan, P. (2017). Varifocal virtuality: a novel optical layout for near-eye display. In SIGGRAPH '17 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Los Angeles, CA, USA, July 30-August 03, 2017. https://doi.org/10.1145/3084822.3084829

Augmented reality (AR) has recently gained momentum in the form of a variety of available optical see-through near-eye displays (NEDs) such as the Meta 2and the Microsoft Hololens. These devices are a big step forward towards Sutherland's vision of a... Read More about Varifocal virtuality: a novel optical layout for near-eye display.

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.

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.

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).

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.

Designing a facial spoofing database for processed image attacks (2016)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2016). Designing a facial spoofing database for processed image attacks. In 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) : Madrid, Spain, 23-25 November 2016 ; proceedings. https://doi.org/10.1049/ic.2016.0073

Face recognition systems are used for user authentication in everyday applications such as logging into a laptop or smartphone without need to memorize a password. However, they are still vulnerable to spoofing attacks, as for example when an imposte... Read More about Designing a facial spoofing database for processed image attacks.

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.

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.

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.

Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images (2016)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2016). Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images. In V. Skala (Ed.),

Liveness tests are techniques employed by face recognition authentication systems, aiming at verifying that a live face rather than a photo is standing in front of the system camera. In this paper, we study the resilience of a standard liveness test... Read More about Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images.

Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks (2015)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2015). Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks. In X. Xie, M. W. Jones, & G. K. L. Tam (Eds.), Proceedings of the 7th UK Computer Vision Student Workshop (BMVW) (5.1-5.9). https://doi.org/10.5244/c.29.bmvw.5

This paper presents an experiment designed to test the resilience of several user verification systems based on face recognition technology against simple identity spoofing methods, such as trying to gain access to the system by using mobile camera s... Read More about Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks.

A steganalytic algorithm for 3D polygonal meshes (2014)
Presentation / Conference Contribution
Yang, Y., Pintus, R., Rushmeier, H., & Ivrissimtzis, I. (2014). A steganalytic algorithm for 3D polygonal meshes. In ICIP 2014 : 2014 IEEE International Conference on Image Processing (ICIP) (4782-4786). https://doi.org/10.1109/icip.2014.7025969

We propose a steganalytic algorithm for watermarks embedded by Cho et al.'s mean-based algorithm [1]. The main observation is that while in a clean model the means of Cho et al.'s normalized histogram bins are expected to follow a Gaussian distributi... Read More about A steganalytic algorithm for 3D polygonal meshes.

Model selection for the Dubuc-Deslauriers family of subdivision schemes (2013)
Presentation / Conference Contribution
Mustafa, G., & Ivrissimtzis, I. (2013). Model selection for the Dubuc-Deslauriers family of subdivision schemes. In R. J. Cripps, G. Mullineux, & M. A. Sabin (Eds.), Mathematics of surfaces - XIV. The proceedings of a conference on the Mathematics of Surfaces, organised by the Institute of Mathematics and its Applications held at the University of Birmingham in September 2013 (155-162)

We propose two methods for model selection for the DubucDeslauriers family of subdivision schemes. In particular, we study model selection under the statistical method of cross-validation and the geometric method of minimizing the absolute discrete t... Read More about Model selection for the Dubuc-Deslauriers family of subdivision schemes.

Mesh Alignment using Grid based PCA (2015)
Presentation / Conference Contribution
Kaye, D., & Ivrissimtzis, I. (2015). Mesh Alignment using Grid based PCA. In J. Braz, J. Pettré, & P. Richard (Eds.), Proceedings of the 10th International Conference on Computer Graphics Theory and Applications (VISIGRAPP 2015) : Berlin, Germany, 11-14 March 2015 (174-181). https://doi.org/10.5220/0005313801740181

We present an algorithm for mesh alignment by performing Principal Components Analysis (PCA) on a set of nodes of a regular 3D grid. The use of a 3D lattice external to both inputs increases the robustness of PCA, particularly when dealing with meshe... Read More about Mesh Alignment using Grid based PCA.

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.

Temporal White-Box Testing Using Evolutionary and Search-base Algorithms (2009)
Presentation / Conference Contribution
Al Moubayed, N., & Awwad Shiekh Hasan, B. (2009, December). Temporal White-Box Testing Using Evolutionary and Search-base Algorithms. Paper presented at 9th Annual Workshop on Computational Intelligence, Colchester, UK

Real-time embedded systems are constrained with real-time requirements. Assuring the quality of such systems is necessary especially in sensitive applications, i.e. where safety is an issue. This paper proposes novel methods for testing the temporal... Read More about Temporal White-Box Testing Using Evolutionary and Search-base Algorithms.