Object recognition in X-ray testing using an efficient search algorithm in multiple views

In order to reduce the security risk of 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, the use of an automated method based on multiple X-ray views is proposed to recognise 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 recognise the objects of interest using matching in all views. The search for matching candidates is efficiently performed using a look-up table that is computed offline. In order to illustrate the effectiveness of the proposed method, experimental results on recognising regular objects (clips, springs and razor blades) in pencil cases are shown achieving high precision and recall (P_r = 95.7% , R_e = 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.