Hilfe Wegweiser Impressum Kontakt Einloggen





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


Schwaninger,  A
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Schwaninger, A., Michel, S., & Bolfing, A. (2007). A statistical approach for image difficulty estimation in x-ray screening using image measurements. In 4th Symposium on Applied Perception in Graphics and Visualization (APGV 2007) (pp. 123-130). New York, NY, USA: ACM Press.

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.