Procedings of the British Machine Vision Conference 2013 2013
DOI: 10.5244/c.27.130
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Object Recognition in Multi-View Dual Energy X-ray Images

Abstract: Object recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. In this paper, we first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-an… Show more

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Cited by 49 publications
(44 citation statements)
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“…This poses an interesting challenge for the use of automatic object recognition approaches akin to the prior work of [1,2,3,4]. In addition, an associated ability to automatically assess the underlying complexity of a given X-ray baggage image facilitates the potential of "auto-clearing" low complexity baggage items (e.g.…”
Section: Introductionmentioning
confidence: 99%
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“…This poses an interesting challenge for the use of automatic object recognition approaches akin to the prior work of [1,2,3,4]. In addition, an associated ability to automatically assess the underlying complexity of a given X-ray baggage image facilitates the potential of "auto-clearing" low complexity baggage items (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Baştan thoroughly reviews several feature detectors (Harris-Laplace, Harris-affine, Hessian-Laplace, Hessianaffine) in his latest work [2], on which he studies applicability and efficiency of sparse local features (SIFT + SPIN [7,9]) on object detection in X-ray baggage imagery via the use of a similar Bag-of-Features concept. This work also investigates how material information given in X-ray imagery via colour mapping ( Figure 1) and multi-view X-ray imaging affect detection performance [3,2]. A related body of work in 3D Computed Tomography (CT) for baggage security has investigated a 3D BoVW approach with suitable extensions to the relevant feature detection and descriptor approaches [10,11,12,13].…”
Section: Introductionmentioning
confidence: 99%
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