Airports are adopting 3D computed tomography (CT)-based technologies for cabin baggage screening to replace traditional 2D X-ray systems. We evaluated a novel virtual reality (VR) 3D object training application using photorealistic 3D models and 3D CT images of prohibited items. Psychology students with no 3D CT screening experience were randomly assigned to the desktop VR (n = 16), head-mounted display (HMD) VR (n = 17), or no training (control group; n = 17) groups. Both training groups underwent six 20-minute training sessions over two weeks. Performance was assessed pre- and post-training using X-ray image interpretation tests. Training experience ratings were collected at every other training session. Both desktop VR and HMD VR training improved performance; HMD VR training also fostered more Presence, Interest/Enjoyment, Perceived Competence, Effort/Importance, and Behavioral Intention; however, Interest/Enjoyment decreased after the initial VR training with both training media. This study demonstrates that VR-based 3D object training can significantly enhance performance and user experience, making it a promising tool for airport security screener training.

Automated explosive detection systems for cabin baggage screening (EDSCB) highlight areas in X-ray images of passenger bags that could contain explosive material. Several countries have implemented EDSCB so that passengers can leave personal electronic devices in their cabin baggage. This increases checkpoint efficiency, but also task difficulty for screeners. We used this case to investigate whether the benefits of decision support systems depend on task difficulty. 100 professional screeners conducted a simulated baggage screening task. They had to detect prohibited articles built into personal electronic devices that were screened either separately (low task difficulty) or inside baggage (high task difficulty). Results showed that EDSCB increased the detection of bombs built into personal electronic devices when screened separately. When electronics were left inside the baggage, operators ignored many EDSCB alarms, and many bombs were missed. Moreover, screeners missed most unalarmed explosives because they over-relied on the EDSCB’s judgment. We recommend that when EDSCB indicates that the bag might contain an explosive, baggage should always be examined further in a secondary search using explosive trace detection, manual opening of bags and other means.

The decoupling of eye vergence and accommodation (V/A) has been found to negatively impact depth interpretation, visual comfort and fatigue. In this paper, we explore a hypothesis that placement of visual cues within a scene can assist a viewer in the process of maintaining the V/A decoupling. This effect is demonstrated through the use of a continuous depth plane that connects spatially distinct scene elements. Our experimental design enables us to make the following three contributions: (1) We show that a continuous depth element can improve the time it takes to transition visual attention in depth. (2) We observe that the subjective assessment of fatigue emerges before we detect a quantitative decline in performance. (3) We aim to motivate that stereoscopic 3D content creators may learn scene composition, framing and montage from visual psychophysics.

Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Several studies have explored the processes underlying human scene categorization, but they have focused on processing global image information. In this study, we present both psychophysical and computational experiments that investigate the role of local versus global image information in scene categorization. In a first set of human experiments, categorization performance is tested when only local or only global image information is present. Our results suggest that humans rely on local, region-based information as much as on global, configural information. In addition, humans seem to integrate both types of information for intact scene categorization. In a set of computational experiments, human performance is compared to two state-of-the-art computer vision approaches that have been shown to be psychophysically plausible and that model either local or global information. In addition to the influence of local versus global information, in a second series of experiments, we investigated the effect of color on the categorization performance of both the human observers and the computational model. Analysis of the human data suggests that color is an additional channel of perceptual information that leads to higher categorization results at the expense of increased reaction times in the intact condition. However, it does not affect reaction times when only local information is present. When color is removed, the employed computational model follows the relative performance decrease of human observers for each scene category and can thus be seen as a perceptually plausible model for human scene categorization based on local image information.

Natural scenes constitute a very heterogeneous stimulus class. Each semantic category contains exemplars of varying typicality. It is, therefore, an interesting question whether humans can categorize natural scenes consistently into a relatively small number of categories, such as, coasts, rivers/lakes, forests, plains, and mountains. This is particularly important for applications, such as, image retrieval systems. Only if typicality is consistently perceived across different individuals, a general image-retrieval system makes sense. In this study, we use psychophysics and computational modeling to gain a deeper understanding of scene typicality. In the first psychophysical experiment, we used a forced-choice categorization task in which each of 250 natural scenes had to be classified into one of the following five categories: coasts, rivers/lakes, forests, plains, and mountains. In the second experiment, the typicality of each scene had to be rated on a 50-point scale for each of the five categories. The psychophysical results show high consistency between participants not only in the categorization of natural scenes, but also in the typicality ratings. In order to model human perception, we then employ a computational approach that uses an intermediatesemantic modeling stepby extracting local semantic concepts, such as, rock, water, and sand. Based on the human typicality ratings, we learn a psychophysically plausible distance measure that leads to a high correlation between the computational and the human ranking of natural scenes. Interestingly, model comparisons without a semantic-modeling step correlated much less with human performance, suggesting that our model is psychophysically very plausible.

For automatic or context-aware systems a major issue is user trust, which is to a large extent determined by system reliability. For systems based on sensor input which are inherently uncertain or even uncomplete there is little hope that they will ever be perfectly reliable. In this paper we test the hypothesis if explicitly displaying the current confidence of the system increases the usability of such systems. For the example of a context-aware mobile phone, the experiments show that displaying confidence information increases the user's trust in the system.