Dr. Lewis D. Griffin Profile

Dr. Lewis D. Griffin

at Univ College London

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Publications (9)

Proceedings Article | 5 October 2017 Paper

Transferring x-ray based automated threat detection between scanners with different energies and resolution

M. Caldwell, M. Ransley, T. Rogers, L. Griffin

Proceedings Volume 10441, 104410F (2017) https://doi.org/10.1117/12.2277641

KEYWORDS: Computer security, Network security, X-rays, X-ray imaging, Image processing, Border security, Machine vision, Computer vision technology, Neural networks, Algorithm development

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Proceedings Article | 1 May 2017 Presentation + Paper

Representation-learning for anomaly detection in complex x-ray cargo imagery

Jerone T. Andrews, Nicolas Jaccard, Thomas Rogers, Lewis Griffin

Proceedings Volume 10187, 101870E (2017) https://doi.org/10.1117/12.2261101

KEYWORDS: X-ray imaging, X-rays, X-ray detectors, Machine learning, Inspection, Algorithm development, Detection and tracking algorithms, Image analysis, Visualization, System on a chip, Image segmentation, Image transmission, Sensors

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Proceedings Article | 1 May 2017 Paper

A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery

Thomas Rogers, Nicolas Jaccard, Lewis Griffin

Proceedings Volume 10187, 101870L (2017) https://doi.org/10.1117/12.2262662

KEYWORDS: RGB color model, Network architectures, Convolutional neural networks, Feature extraction, Transparency, Signal attenuation, X-rays, Image processing, X-ray imaging, Neural networks, Inspection, Computer vision technology, Machine learning, Materials analysis

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Proceedings Article | 12 May 2016 Paper

Tackling the x-ray cargo inspection challenge using machine learning

Nicolas Jaccard, Thomas Rogers, Edward Morton, Lewis Griffin

Proceedings Volume 9847, 98470N (2016) https://doi.org/10.1117/12.2222765

KEYWORDS: Signal attenuation, Inspection, X-rays, X-ray imaging, Image processing, Machine learning, System on a chip, Image segmentation, Visualization, Detection and tracking algorithms

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Proceedings Article | 11 March 2008 Paper

A novel framework for multi-modal intensity-based similarity measures based on internal similarity

Graeme Penney, Lewis Griffin, Andrew King, David Hawkes

Proceedings Volume 6914, 69140X (2008) https://doi.org/10.1117/12.769402

KEYWORDS: Image registration, Image enhancement, 3D image enhancement, Image processing, Ultrasonography, Magnetic resonance imaging, 3D image processing, Computed tomography, Bone, Gold

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We present a novel framework for describing intensity-based multi-modal similarity measures. Our framework is based around a concept of internal, or self, similarity. Firstly the locations of multiple regions or patches which are "similar" to each other are identified within a single image. The term "similar" is used here to represent a generic intra-modal similarity measure. Then if we examine a second image in the same locations, and this image is registered to the first image, we should find that the patches in these locations are also "similar", though the actual features in the patches when compared between the images could be very different. We propose that a measure based on this principle could be used as an inter-modal similarity measure because, as the two images become increasingly misregistered then the patches within the second image should become increasingly dissimilar. Therefore, our framework results in an inter-modal similarity measure by using two intra-modal similarity measures applied separately within each image. In this paper we describe how popular multi-modal similarity measures such as mutual information can be described within this framework. In addition the framework has the potential to allow the formation of novel similarity measures which can register using regional information, rather than individual pixel/voxel intensities. An example similarity measure is produced and its ability to guide a registration algorithm is investigated. Registration experiments are carried out using three datasets. The pairs of images to be registered were specifically chosen as they were expected to challenge (i.e. result in misregistrations) standard intensity-based measures, such as mutual information. The images include synthetic data, cadaver data and clinical data and cover a range of modalities. Our experiments show that our proposed measure is able to achieve accurate registrations where standard intensity-based measures, such as mutual information, fail.

Proceedings Article | 12 February 2007 Paper

Feature category systems for 2nd order local image structure induced by natural image statistics and otherwise

Lewis Griffin, Martin Lillholm

Proceedings Volume 6492, 649209 (2007) https://doi.org/10.1117/12.714560

KEYWORDS: Neodymium, Solids, Quantization, Prototyping, Visual process modeling, Computing systems, Gaussian filters, Electronic imaging, Image filtering, Convolution

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Proceedings Article | 4 May 2004 Paper

Depth perception of stereo overlays in image-guided surgery

Laura Johnson, Philip Edwards, Lewis Griffin, David Hawkes

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.535138

KEYWORDS: Surgery, Calibration, Error analysis, Skin, Image-guided intervention, Visualization, Eye, Natural surfaces, Computing systems, Cones

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Proceedings Article | 22 September 1992 Paper

Structure-sensitive scale and the hierarchical segmentation of gray-level images

Lewis Griffin, Alan Colchester, Glynn Robinson, David Hawkes

Proceedings Volume 1808, (1992) https://doi.org/10.1117/12.131064

KEYWORDS: Image segmentation, Visualization, Biomedical optics, Magnetic resonance imaging, Stochastic processes, Gaussian filters, Image resolution, Image processing, Spatial frequencies, Linear filtering

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Proceedings Article | 26 June 1992 Paper

Hierarchical shape representation for use in anatomical object recognition

Glynn Robinson, Alan Colchester, Lewis Griffin

Proceedings Volume 1660, (1992) https://doi.org/10.1117/12.59588

KEYWORDS: Image processing, Biomedical optics, 3D image processing, Microscopy, Image segmentation, Solids, Object recognition, Medical imaging, Magnetic resonance imaging, Human subjects

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Showing 5 of 9 publications

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