首页> 外文会议>2011 IEEE International Carnahan Conference on Security Technology >Does the application of virtually merged images influence the effectiveness of computer-based training in x-ray screening?
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Does the application of virtually merged images influence the effectiveness of computer-based training in x-ray screening?

机译:虚拟合并的图像的应用是否会影响X射线筛选中基于计算机的培训的有效性?

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The necessity of computer based training for airport security screening officers to achieve and maintain a high level of x-ray image interpretation competency is well-known. During such training, x-ray images of passenger bags, similar to how they appear at the security checkpoint, are presented to the screening officers on a screen and have to be judged regarding their contents. Certain computer based training systems, such as X-Ray Tutor (XRT), apply a special algorithm that automatically merges images of fictional threat items (FTIs) into x-ray images of passenger bags. The advantage of virtual image merging is that a) a huge variety of different bag images containing different threats can be created, and b) the difficulty of threats (e.g. viewpoint, superposition and bag complexity) can be adapted to individual performance and learning progress. However, merging images virtually can lead to artifacts appearing on the resulting pictures. Therefore, the question arises if the training with merged images actually reflects the reality or if the resulting artifacts on the images actually make it easier to detect threat items during training. If this would be the case, the actual effectiveness of such a CBT would have to be questioned. The aim of this study was to investigate whether threat items that are virtually merged into bags (as they appear during training), are detected more easily than threat items physically embedded in bags and x-rayed as a whole (like at the security checkpoint). A test was conducted with screeners at different international airports. 256 images of passenger bags were presented to screeners, 128 of them contained threat items. Half of these threat images were created through virtual merging, while for the other half of the images threat items had been placed physically into the bags. Half of the used threat items were familiar to the screeners from training (XRT-library), whereas the other half of the items were new. The same study was rep--licated one year later. In both studies, the screeners achieved high detection performance scores in the test. However, only very small differences in detection performance for the virtually merged threat items and the physically embedded threat items were found. In fact, detection performance was even slightly higher for the physically embedded threat items. In summary, results imply that when well elaborated merging algorithms are used, small artifacts appearing on the test images influence neither the effectiveness of CBT nor the detection of real threat items in real x-ray images negatively.
机译:众所周知,对机场安全检查人员进行基于计算机的培训以达到并保持高水平的X射线图像解释能力是必要的。在这样的训练过程中,类似于在安全检查站出现的行李袋的X射线图像会在屏幕上呈现给检查人员,并且必须对其内容进行判断。某些基于计算机的培训系统,例如X射线导师(XRT),应用了一种特殊算法,该算法会自动将虚构威胁物品(FTI)的图像合并为乘客行李的X射线图像。虚拟图像合并的优势在于:a)可以创建包含不同威胁的各种各样的不同袋图像,并且b)可以将威胁的难度(例如视点,叠加和袋的复杂性)调整为适合个人表现和学习进度。但是,实际上合并图像可能会导致在生成的图片上出现伪像。因此,如果合并图像的训练实际上反映了现实,或者图像上产生的伪像实际上使训练过程中更容易检测到威胁项目,就会出现问题。如果是这种情况,则必须质疑这种CBT的实际有效性。这项研究的目的是调查与包装在袋中并整体进行X射线检查的威胁项目相比,虚拟合并到袋子中的威胁项目(如在训练过程中出现的威胁)是否更容易被检测到(例如在安全检查站) 。在不同的国际机场对筛选器进行了测试。向放映人员展示了256张乘客行李图像,其中128张装有威胁物品。这些威胁图像的一半是通过虚拟合并创建的,而另一半图像是将威胁物品实际放置在袋子中的。筛选人员从培训(XRT库)熟悉了一半使用的威胁项目,而另一半则是新的。相同的研究代表了 -- 一年后被许可。在两项研究中,筛选器在测试中均获得了较高的检测性能得分。但是,对于虚拟合并的威胁项目和物理上嵌入的威胁项目,在检测性能方面只发现了很小的差异。实际上,对于物理嵌入的威胁项目,检测性能甚至更高。总而言之,结果表明,当使用精心设计的合并算法时,出现在测试图像上的小伪影既不会影响CBT的有效性,也不会负面影响真实X射线图像中真实威胁项的检测。

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