How Image Based Factors and Human Factors Contribute to Threat Detection Performance in X-Ray Aviation Security Screening

The present study examines the relative importance of a series of known and expected factors that highly affect threat detection performance in aviation security X-ray screening. Examined image based factors were threat item view difficulty, threat item superposition, bag complexity and bag size. Further, also the two human/demographic factors X-ray image interpretation training and age were examined. Image measurements and performance statistics for factors estimation are introduced. Three statistical approaches were applied in order to examine the impact of the introduced factors on threat detection performance and revealed consistent results. Bivariate correlations between detection performance and predictors/factors were analysed to estimate the isolated impact of each single factor independently of any other. Multiple linear regression analyses were applied for estimating the overall impact of all image based factors and human/demographic factors respectively. And analyses of covariance were applied in order to check for possible interaction effects between all factors of the models. All analyses were applied separately for the four threat item categories guns, knives, improvised explosive devices and other

A statistical approach for image difficulty estimation in x-ray screening using image measurements

The relevance of aviation security has increased dramatically at the beginning of this century. One of the most important tasks is the visual inspection of passenger bags using x-ray machines. In this study, we investigated the role of image based factors on human detection of prohibited items in x-ray images. Schwaninger, Hardmeier, and Hofer (2004, 2005) have identified three image based factors: View Difficulty, Superposition and Bag Complexity. This article consists of 4 experiments which lead to the development of a statistical model that is able to predict image difficulty based on these image based factors. Experiment 1 is a replication of earlier findings confirming the relevance of image based factors as defined by Schwaninger et al. (2005) on x-ray detection performance. In Experiment 2, we found significant correlations between human ratings of image based factors and human detection performance. In Experiment 3, we introduced our image measurements and found significant correlations between them a nd human detection performance. Moreover, significant correlations were found between our image measurements and corresponding human ratings, indicating high perceptual plausibility. In Experiment 4, it was shown using multiple linear regression analysis that our image measurements can predict human performance as well as human ratings can. Applications of a computational model for threat image projection systems and for adaptive computer-based training are discussed

Towards a model for estimating image difficulty in x-ray screening

In this study we developed a first computational model for estimating image difficulty of x-ray images of passenger bags. Based on [1] three image-based factors are proposed as predictors of image difficulty: view difficulty of the threat item, superposition by other objects, and bag complexity (i.e. clutter and transparency of the bag). First, these factors were validated using detection experiments. We then developed computer-based algorithms to estimate the image-based factors automatically. Finally, we could show that our computational model can better explain human performance than human ratings of the imagebased factors

How Much Water is in the Hill Country?

The Hill Country is a unique region of Texas where rivers rise out of the limestone, spilling the means for life onto what would be an otherwise dry and difficult place to survive. The conservation of the Hill Country’s hydrologic systems is not only important to protecting the diverse wildlife indigenous to this area but also to the growing population moving into the expanding urban corridor between Austin and San Antonio and west into the Hill Country. The current period of prolonged drought has depleted many reservoir levels to historic lows and created a growing reliance on groundwater to support the escalating population of Central Texas. Since there are few regulations that can be placed on aquifer pumping, there is a very real possibility that unsustainable groundwater development and drought could endanger major springs that are instrumental to the base flow of the major rivers in the Hill Country region. There is still much to learn about the interconnected nature of these aquifers, rivers and lakes. The purpose of this project was to develop a methodology for hydrogeologic research that will help scientists, decision-makers, and stakeholders better understand how the aquifers, springs, and rivers in the Hill Country interact.The Meadows Center for Water and the Environmen

The Impact of Image Based Factors and Training on Threat Detection Performance in X-Ray Screening

In this study, two experiments are reported which investigated the relative importance of five different image based factors and one human factor (training) in mediating threat detection performance of human operators in airport security x-ray screening. Experiment 1 was based on a random sample of roughly 16’000 records of threat image projection (TIP) data. TIP is a software function available on state-of-the-art x-ray screening equipment that allows the projection of fictional threat images (FTIs) into x-ray images of passenger bags during the routine baggage screening operation. Analysis of main effects showed that image based factors can substantially affect screener detection performance in terms of the hit rate (identification of FTIs). There were strong effects of FTI view difficulty (rotation of FTIs) and superposition of FTIs by other objects in the x-ray image of a passenger bag. The amount of opacity in the x-ray image of a passenger bag had a small although significant effect on detection performance. The two image based factors clutter and bag size did not have a significant effect. Experiment 2 was conducted using an offline-test in order to provide controlled and more detailed data for analyzing the image based factors from Experiment 1, as well as the human factor of training. In particular the individual factors’ main effects on detection performance, main effects of all factors taken together and factor interactions were analyzed. In the test design the following image-based factors were varied systematically: Threat (FTI) category (guns, knives, improvised explosive devices, other threats), view difficulty, superposition, bag complexity (a combination of opacity and clutter) and bag size. Data were collected from 200 screening officers at five sites across Europe. For screener training all five sites use the same computer-based training system. Consistent with the results obtained in Experiment 1, there were large main effects of threat (FTI) category, view difficulty, and superposition. Again consistent with Experiment 1, effects of bag complexity (opacity and clutter) and bag size were much smaller. In addition to Experiment 1, the number of computer based training (CBT) hours was available for each security officer participating in the study. Training turned out to be a key driver to improving threat detection performance in x-ray screening and seemed to mediate the effects of some image based factors. Possible implications regarding the enhancement of human-machine interaction in x-ray screening are discussed

Discovering the “customer annoyance iceberg” through evidence controlling

Complaint management, Service controlling, Complaining behavior, Customer annoyance, Customer dissatisfaction,