Motivated by aiding human operators in the detection of dangerous objects in passenger luggage, such as in airports, we develop an automatic object detection approach for multi-view X-ray image data. We make three main contributions: First, we systematically analyze the appearance variations of objects in X-ray images from inspection systems. We then address these variations by adapting standard appearance-based object detection approaches to the specifics of dual-energy X-ray data and the inspection scenario itself. To that end we reduce projection distortions, extend the feature representation, and address both in-plane and out-of-plane object rotations, which are a key challenge compared to many detection tasks in photographic images. Finally, we propose a novel multi-view (multi-camera) detection approach that combines single-view detections from multiple views and takes advantage of the mutual reinforcement of geometrically consistent hypotheses. While our multi-view approach can be used atop arbitrary single-view detectors, thus also for multi-camera detection in photographic images, we evaluate our method on detecting handguns in carry-on luggage. Our results show significant performance gains from all components. © 2012 Springer-Verlag.
CITATION STYLE
Franzel, T., Schmidt, U., & Roth, S. (2012). Object detection in multi-view X-ray images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7476 LNCS, pp. 144–154). https://doi.org/10.1007/978-3-642-32717-9_15
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