Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
November 10, 2022 ยท The Cartographer ยท ๐ IEEE Access
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"Title-pattern auto-detect: Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Su"
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Authors
Mehdi Rafiei, Jenni Raitoharju, Alexandros Iosifidis
arXiv ID
2211.05565
Category
cs.CV: Computer Vision
Citations
35
Venue
IEEE Access
Last Checked
2 days ago
Abstract
X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.
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