Browsing by Author "Riffo, V."

Now showing 1 - 5 of 5
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    Active X-ray testing of complex objects
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2012) Riffo, V.; Mery, D.
    X-ray testing of complex objects - such as luggage screening at airports - is usually performed manually. This is not always effective, since it depends strongly on the pose of the objects of interest (target objects) and occlusion, as well as human capabilities. Additionally, certain target objects are difficult to detect using only one viewpoint. For this reason, we have developed an active X-ray testing framework that is able to find an adequate viewpoint of the target object in order to obtain better X-ray images to analyse. The key idea of our method is to adapt automatically the viewpoint of the X-ray images in order to project the target object in poses where the detection performance should be higher. Thus, the detection inside complex objects can be performed in a more effective way. Using a robotic arm and a semiautomatic manipulator system, the robustness and reliability of the method have been verified in the automated detection of razor blades located inside nine different objects, showing promising preliminary results: in 130 experiments we were able to detect the razor blade 115 times with 10 false alarms, achieving a recall of 89% and a precision of 92%.
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    Automated x-ray object recognition using an efficient search algorithm in multiple views
    (2013) Mery Quiroz, Domingo Arturo; Riffo, V.; Zuccar, I.; Pieringer, C.
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    Handgun Detection in Single-Spectrum Multiple X-ray Views Based on 3D Object Recognition
    (2019) Riffo, V.; Godoy, I.; Mery Quiroz, Domingo
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    Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection
    (2017) Mery Quiroz, Domingo; Svec, Erick; Arias Figueroa, Marco Antonio; Riffo, V.; Saavedra, J. M.; Banerjee, S.
    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. Generally, human operators are in charge of these tasks as automated recognition in baggage inspection is still far from perfect. Research and development on X-ray testing is, however, exploring new approaches based on computer vision that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition in X-ray testing by evaluating different computer vision strategies that have been proposed in the last years. We tested ten approaches. They are based on bag of words, sparse representations, deep learning, and classic pattern recognition schemes among others. For each method, we: 1) present a brief explanation; 2) show experimental results on the same database; and 3) provide concluding remarks discussing pros and cons of each method. In order to make fair comparisons, we define a common experimental protocol based on training, validation, and testing data (selected from the public GDXray database). The effectiveness of each method was tested in the recognition of three different threat objects: 1) handguns; 2) shuriken (ninja stars); and 3) razor blades. In our experiments, the highest recognition rate was achieved by methods based on visual vocabularies and deep features with more than 95% of accuracy. We strongly believe that it is possible to design an automated aid for the human inspection task using these computer vision algorithms.
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    Object recognition in X-ray testing using an efficient search algorithm in multiple views
    (2017) Pieringer Baeza, Christian Philip; Zuccar, I.; Riffo, V.; Mery Quiroz, Domingo