Artikel: Robust image descriptor for machine learning based data reduction in serial crystallography.
Journal of applied crystallography
2024 Band 57, Heft Pt 2, Seite(n) 413–430
Abstract: Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 ... ...
| Abstract | Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as 'hit' and 'miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers. |
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| Sprache | Englisch |
| Erscheinungsdatum | 2024-03-26 |
| Erscheinungsland | United States |
| Dokumenttyp | Journal Article |
| ZDB-ID | 2020879-0 |
| ISSN | 1600-5767 ; 0021-8898 |
| ISSN (online) | 1600-5767 |
| ISSN | 0021-8898 |
| DOI | 10.1107/S160057672400147X |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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