Object Recognition in Baggage Inspection Using Adaptive Sparse Representations of X-ray Images

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several random patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, random test patches of the quer...

• Type: Journal
• Year: 2015
• Where: PSIVT
• Authors: Domingo Mery, Erick Svec, Marco Arias
• Keywords: Multimedia, PSIVT 2015