TY - GEN
T1 - Automated x-ray object recognition using an efficient search algorithm in multiple views
AU - Mery, Domingo
AU - Riffo, Vladimir
AU - Zuccar, Irene
AU - Pieringer, Christian
PY - 2013
Y1 - 2013
N2 - In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple X-ray views to recognize certain regular objects with highly defined shapes and sizes. The method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. The search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects-clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.
AB - In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple X-ray views to recognize certain regular objects with highly defined shapes and sizes. The method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. The search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects-clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.
KW - 3D object recognition
KW - baggage screening
KW - multiple view analysis
KW - X-ray testing
UR - http://www.scopus.com/inward/record.url?scp=84884919620&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2013.62
DO - 10.1109/CVPRW.2013.62
M3 - Conference contribution
AN - SCOPUS:84884919620
SN - 9780769549903
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 368
EP - 374
BT - Proceedings - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
T2 - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
Y2 - 23 June 2013 through 28 June 2013
ER -