Browsing by Author "Prasad, M."

Now showing 1 - 4 of 4
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    A fast and self-adaptive on-line learning detection system
    (2018) Prasad, M.; Zheng, D.-R.; Mery Quiroz, Domingo Arturo; Puthal, D.; Sundaram, S.; Lin, C.-T.
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    A novel online self-learning system with automatic object detection model for multimedia applications
    (2020) Cheng, E. J.; Prasad, M.; Yang, J.; Zheng, D. R.; Tao, X.; Mery Quiroz, Domingo; Young, K. Y.; Lin, C. T.
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    A Robust Face Recognition System for One Sample Problem
    (2019) Meena, M.S.; Singh, P.; Rana, A.; Mery Quiroz, Domingo Arturo; Prasad, M.
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    X-Ray Baggage Inspection With Computer Vision: A Survey
    (IEEE, 2020) Mery Quiroz, Domingo; Saavedra, D.; Prasad, M.
    In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem: i) X-ray energies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required; ii) X-ray multi-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; and iii) X-ray computer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.