Development of a detector model for generation of synthetic radiographs of cargo containers

White, Timothy A.; Bredt, Ofelia P.; Schweppe, John E.; Runkle, Robert C.

doi:10.1016/j.nimb.2008.03.184

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…For example, it is difficult to generate out-of-plane rotations, so augmentation is usually limited to in-plane rotations of the staged threat items. Potential remedies include either developing a framework for collecting the optimal number of threat poses to make accurate interpolation of intermediate out-of-plane rotations, or generating realistic threat radiography from realistic 3D CAD models of threats [83,30]. The requirements for a solution are that out-of-plane rotations are accurate, realistic, and can be computed efficiently or on-the-fly.…”

Section: Discussion On Image Preprocessingmentioning

confidence: 99%

“…They also combine multiple random objects to form a composite object. The composite object is projected into real empty container images in a similar way to White et al [82].…”

Section: Threat Image Projection (Tip)mentioning

confidence: 99%

“…In cargo, some authors have suggested methods for image synthesis for other purposes, but which could be applicable to TIP. White et al [82] introduce a method for generating synthetic γ-ray cargo radiographs, and use it as surrogate data for testing the effectiveness of different scanning systems when it is impractical to collect large amounts of empirical data. The authors derive an empirical model of the imaging system response from real radiographs of well-characterised objects.…”

Section: Threat Image Projection (Tip)mentioning

confidence: 99%

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Automated X-ray image analysis for cargo security: Critical review and future promise

Rogers

Jaccard

Morton³

et al. 2017

XST

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We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs-and securityrelated threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

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Section: Discussion On Image Preprocessingmentioning

confidence: 99%

“…They also combine multiple random objects to form a composite object. The composite object is projected into real empty container images in a similar way to White et al [82].…”

Section: Threat Image Projection (Tip)mentioning

confidence: 99%

Section: Threat Image Projection (Tip)mentioning

confidence: 99%

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Automated X-ray image analysis for cargo security: Critical review and future promise

Rogers

Jaccard

Morton³

et al. 2017

XST

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“…Data can also be entirely synthesised from digital models using X-ray simulation techniques. 20 Benign images may also be staged or synthesised, as well as drawing from routine stream of commerce. A dataset for ATD training or testing consists of a set of training/test images.…”

Section: Ontology Of Data For X-ray Threat Detectionmentioning

confidence: 99%

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

Caldwell

Ransley

Rogers

et al. 2017

Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies

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A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners.

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Scatter in cargo radiography

Miller

Caggiano

Runkle

et al. 2011

Applied Radiation and Isotopes

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Development of a detector model for generation of synthetic radiographs of cargo containers

Cited by 4 publications

References 8 publications

Automated X-ray image analysis for cargo security: Critical review and future promise

Automated X-ray image analysis for cargo security: Critical review and future promise

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

Scatter in cargo radiography

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