1 Object recognition in X-ray testing using an efficient search algorithm in multiple views Domingo Mery, Vladimir Riffo, Irene Zuccar, Christian Pieringer Abstract—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 high precision and recall (Pr = 95.7% , Re = 92.5%) for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm. Index Terms—X-ray testing, computer vision, baggage inspection, image analysis. ✦ 1 I NTRODUCTION B AGGAGE inspection using X-ray screening is a priority task that reduces the risk of crime, terrorist attacks and propagation of pests and dis- eases [42]. Security and safety screening with X-ray scanners has become an important process in public spaces and at border checkpoints [30]. However, inspection is a complex task because threat items are very difficult to detect when placed in closely packed bags, occluded by other objects, or rotated, thus presenting an unrecognizable view [5]. Manual detection of threat items by human inspectors is extremely demanding [34]. It is tedious because very few bags actually contain threat items, and it is stressful because the work of identifying a wide range of objects, shapes and substances (metals, • D. Mery is with the Department of Computer Science Department (DCC), Pontificia Universidad Cat´ olica, Vicu˜ na Mackenna 4860, Santiago Chile. E-mail: dmery@ing.puc.cl URL: http://dmery.ing. puc.cl • V. Riffo is with DIICC–Universidad de Atacama, Copiap´ o, Chile • I. Zuccar is with DIINF–Universidad de Santiago de Chile, Santiago, Chile • C. Pieringer is with DCC–Pontificia Universidad Cat´ olica de Chile, Santiago, Chile organic and inorganic substances) takes a great deal of concentration. In addition, human inspec- tors receive only minimal technological support. Furthermore, during rush hours, they have only a few seconds to decide whether or not a bag contains a threat item [4]. Since each operator must screen many bags, the likelihood of human error becomes considerable over a long period of time even with intensive training. The literature suggests that detection performance is only about 80–90% [22]. In baggage inspection, automated X-ray test- ing remains an open question due to: i) loss of generality, which means that approaches developed for one task may not transfer well to another; ii) deficient detection accuracy, which means that there is a fundamental tradeoff between false alarms and missed detections; iii) limited robustness given that requirements for the use of a method are often met for simple structures only; and iv) low adaptiveness in that it may be very difficult to accommodate an automated system to design modifications or different specimens. Before 9/11, the X-ray analysis of luggage mainly focused on capturing the images of their content: the reader can find in [26] an interesting analy- sis carried out in 1989 of several aircraft attacks around the world, and the existing technologies to