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Object detection in multi-view X-ray images

: Franzel, Thorsten; Schmidt, Uwe; Roth, Stefan


Pinz, Axel (Ed.):
Pattern recognition. Joint 34th DAGM and 36th OAGM symposium 2012 : Graz, Austria, August 28 - 31, 2012; proceedings
Berlin: Springer, 2012 (Lecture Notes in Computer Science 7476)
ISBN: 978-3-642-32716-2
ISBN: 3-642-32716-8
ISBN: 978-3-642-32717-9
ISSN: 0302-9743
German Association for Pattern Recognition (DAGM Symposium) <34, 2012, Graz>
Austrian Association for Pattern Recognition (OAGM Symposium) <36, 2012, Graz>
Conference Paper
Fraunhofer IGD ()
object detection; x-ray; detection; security enforcement; Forschungsgruppe Visual Inference (VINF)

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.