Object Recognition in X-ray Testing Using Adaptive Sparse Representations
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2016/07/26
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Indian UGC (journal)
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Refrences29
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Citations16
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Domingo Mery ORCID (unauthenticated)
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Erick Svec
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Marco Arias
Cite
Mery, Domingo, et al. “Object Recognition in X-Ray Testing Using Adaptive Sparse Representations”. Journal of Nondestructive Evaluation, vol. 35, no. 3, 2016, https://doi.org/10.1007/s10921-016-0362-8.
Mery, D., Svec, E., & Arias, M. (2016). Object Recognition in X-ray Testing Using Adaptive Sparse Representations. Journal of Nondestructive Evaluation, 35(3). https://doi.org/10.1007/s10921-016-0362-8
Mery D, Svec E, Arias M. Object Recognition in X-ray Testing Using Adaptive Sparse Representations. Journal of Nondestructive Evaluation. 2016;35(3).
Journal Category
Technology
Electrical engineering
Electronics
Nuclear engineering
Materials of engineering and construction
Mechanics of materials
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| 10.1109/TSMC.2015.2439233 | 2016 | |||
| Object classification in 3D baggage security computed tomography imagery using visual codebooks | Pattern Recognition |
| 23 | 2015 |
| GDXray: The Database of X-ray Images for Nondestructive Testing | Journal of Nondestructive Evaluation |
| 195 | 2015 |
| 10.1109/TMECH.2014.2311032 | IEEE/ASME Transactions on Mechatronics |
| 2015 | |
| A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery | Pattern Recognition |
| 50 | 2013 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| MC-CDPNet: Multi-Channel Correlated Detail Preserving Network for X-Ray-Based Baggage Screening | Journal of Nondestructive Evaluation |
| 2 | 2023 |
Automated detection of inorganic powders in X-ray images of airport luggage | Journal of Transportation Security |
| 2023 | |
Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks | Scientific Reports |
| 2023 | |
| Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey | IEEE Access |
| 2023 | |
| A nondestructive automatic defect detection method with pixelwise segmentation | Knowledge-Based Systems |
| 43 | 2022 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials 5
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Threat Objects Detection in X-ray Images Using an Active Vision Approach and was published in 2017. The most recent citation comes from a 2023 study titled Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks. This article reached its peak citation in 2022, with 7 citations. It has been cited in 11 different journals, 27% of which are open access. Among related journals, the Journal of Nondestructive Evaluation cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.