Inspection of Complex Objects Using Multiple-X-Ray Views. Mery, D. IEEE/ASME Transactions on Mechatronics, 20(1):338-347, 2015. ![Inspection of Complex Objects Using Multiple-X-Ray Views [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex This paper presents a new methodology for identifying parts of interest inside of a complex object using multiple X-ray views. The proposed method consists of five steps: A) \em image acquisition, that acquires an image sequence where the parts of the object are captured from different viewpoints; B) \em geometric model estimation, that establishes a multiple view geometric model used to find the correct correspondence among different views; C) \em single view detection, that segment potential regions of interest in each view; D) \em multiple view detection, that matches and tracks potential regions based on similarity and geometrical multiple view constraints; and E) \em analysis, that analyzes the tracked regions using multiple view information, filtering out false alarms without eliminating existing parts of interest. In order to evaluate the effectiveness of the proposed method, the algorithm was tested on 32 cases (five applications using different segmentation approaches) yielding promising results: precision and recall were 95.7% and 93.9%, respectively. Additionally, the multiple view information obtained from the tracked parts was effectively used for recognition purposes. In our recognition experiments, we obtained an accuracy of 96.5%. Validation experiments show that our approach achieves better performance than other representative methods in the literature.
@article{Mery2015:IEEE-Mechatronics,
author = {Mery, D.},
title = {{Inspection of Complex Objects Using Multiple-X-Ray Views}},
journal = {IEEE/ASME Transactions on Mechatronics},
year = {2015},
volume = {20},
number = {1},
pages = {338-347},
url = {http://dmery.sitios.ing.uc.cl/Prints/ISI-Journals/2014-IEEE-ComplexObjects.pdf},
abstract = {This paper presents a new methodology for identifying parts of interest inside of a complex object using multiple X-ray views. The proposed method consists of five steps: A) {\em image acquisition}, that acquires an image sequence where the parts of the object are captured from different viewpoints; B) {\em geometric model estimation}, that establishes a multiple view geometric model used to find the correct correspondence among different views; C) {\em single view detection}, that segment potential regions of interest in each view; D) {\em multiple view detection}, that matches and tracks potential regions based on similarity and geometrical multiple view constraints; and E) {\em analysis}, that analyzes the tracked regions using multiple view information, filtering out false alarms without eliminating existing parts of interest. In order to evaluate the effectiveness of the proposed method, the algorithm was tested on 32 cases (five applications using different segmentation approaches) yielding promising results: precision and recall were 95.7\% and 93.9\%, respectively. Additionally, the multiple view information obtained from the tracked parts was effectively used for recognition purposes. In our recognition experiments, we obtained an accuracy of 96.5\%. Validation experiments show that our approach achieves better performance than other representative methods in the literature.
}
}
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