Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data ; volume:28 ; number:3 ; year:2023
E-Journal of nondestructive testing ; 28, Heft 3 (2023)
- Creator
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Schön, Tobias
Gosswami, Bishwajit Mohan
Hvingelby, Rasmus
Suth, Daniel
Kemeter, Lukas Malte
Sierak, Paulina
- DOI
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10.58286/27732
- URN
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urn:nbn:de:101:1-2024041112492070254101
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:49 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Schön, Tobias
- Gosswami, Bishwajit Mohan
- Hvingelby, Rasmus
- Suth, Daniel
- Kemeter, Lukas Malte
- Sierak, Paulina
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Verbal projections
Automated 3D Defect Detectionm based on Simulated Reference
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Verbal projections
Automated 3D Defect Detectionm based on Simulated Reference
Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs
Discovery of Unconventional Proton‐Conducting Inorganic Solids via Defect‐Chemistry‐Trained, Interpretable Machine Learning
Unsupervised deep learning for defect detection on CT parts using simulated data
Simulated eye height impacts size perception differently depending on real-world posture
Projections
Projections
Incorporation of RCM-simulated spatial details into climate change projections derived from global climate models
Physiological responses during simulated 16 km recumbent handcycling time trial and determinants of performance in trained handcyclists
X-Ray Scatter Correction by Deep Semi-supervised Learning of Simulated Projections with Beam-hole Array
Reverse projections