Automatic Visual Inspection: An Approach with Multi-Instance Learning. Mery, D., Riffo, V., Zuccar, I., & Pieringer, C. Insight. (accepted in October 2016)
abstract   bibtex   
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.
@article{Mery2016:Insight,
  title={Automatic Visual Inspection: An Approach with Multi-Instance Learning},
author={Mery, D. and Riffo, V. and Zuccar, I. and Pieringer, C.}, 
  journal={Insight},
  note = {(accepted in October 2016)},
  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.}
}

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