Browsing by Author "Flitton, Greg T."

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    A 3D extension to cortex like mechanisms for 3D object class recognition
    (2012-06-21T00:00:00Z) Flitton, Greg T.; Breckon, Toby P.; Megherbi, Najla
    We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items
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    A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery
    (Elsevier, 2013-02-16) Flitton, Greg T.; Breckon, Toby P.; Megherbi, Najla
    We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects.
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    A comparison of classification approaches for threat detection in CT based baggage screening
    (IEEE, 2013-02-21) Megherbi, Najla; Han, Jiwan; Breckon, Toby P.; Flitton, Greg T.
    Computed Tomography (CT) based baggage security screening systems are of increasing use in transportation security. The ability to automatically identify potential threat item is a key aspect of current research in this area. Here we present a comparison of varying classification approaches for the automated detection of threat objects in cluttered 3D CT imagery from such security screening systems. By combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods we compare five varying final classification stage approaches and present significant performance achievements in the automated detection of specified exemplar items.
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    Extending computer vision techniques to recognition problems in 3d volumetric baggage imagery
    (Cranfield University, 2012-01) Flitton, Greg T.; Breckon, Toby P.
    We investigate the application of computer vision techniques to rigid object recognition in Computed Tomography (CT) security scans of baggage items. This imagery is of poor resolution and is complex in nature: items of interest can be imaged in any orientation and copious amounts of clutter, noise and artefacts are prevalent. We begin with a novel 3D extension to the seminal SIFT keypoint descriptor that is evaluated through specific instance recognition in the volumetric data. We subsequently compare the performance of the SIFT descriptor against a selection of alternative descriptor methodologies. We demonstrate that the 3D SIFT descriptor is notably outperformed by simpler descriptors which appear to be more suited for use in noise and artefact-prone CT imagery. Rigid object class recognition in 3D volumetric baggage data has received little attention in prior work. We evaluate contrasting techniques between a traditional approach derived from interest point descriptors and a novel technique based on modelling of the primary components of the primate visual cortex. We initially demonstrate class recognition through the implementation of a codebook approach. A variety of aspects relating to codebook generation are investigated (codebook size, assignment method) using a range of feature descriptors. Recognition of a number of object classes is performed and results from this show that the choice of descriptor is a critical aspect. Finally, we present a unique extension to the established standard model of the visual cortex: a volumetric implementation. The visual cortex model comprises a hierarchical structure of alternating simple and complex operations that has demonstrated excellent class recognition results using 2D imagery. We derive 3D extensions to each layer in the hierarchy resulting in class recognition results that signficantly outperform those achieved using the earlier traditional codebook approach. Overall we present several novel solutions to object recognition within 3D CT security images that are supported by strong statistical results.
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    A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery
    (IEEE, 2013-02-21) Mouton, Andre; Megherbi, Najla; Flitton, Greg T.; Bizot, Suzanne; Breckon, Toby P.
    This paper introduces a novel technique for Metal Artefact Reduction (MAR) in the previously unconsidered context 3D CT baggage imagery. The output of a conventional sinogram completion-based MAR approach is refined by imposing an upper limit on the intensity of the corrected images and by performing post-filtering using the non-local means filter. Furthermore, performance is evaluated using a novel quantitative analysis technique, using the ratio of noisy 3D SIFT detection points identified, as well as a standard qualitative comparison (visual quality). The objective of the quantitative analysis is to evaluate the impact of MAR on the application of computer vision techniques for automatic object recognition. The study yields encouraging results in both the qualitative and quantitative analyses. The proposed method yields a significant improvement in performance when compared to algorithms based on linear interpolation and reprojection-reconstruction; especially in terms of reducing the occurrence of new artefacts in the corrected images. The results serve as a strong indication that MAR will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.