ML-TN-004 — Machine Learning, spectroscopy, and materials classification
Revision as of 13:03, 24 September 2021 by U0001 (talk | contribs) (U0001 moved page ML-TN-003 — Machine Learning, spectroscopy, and material detection to ML-TN-003 — Machine Learning, spectroscopy, and materials classification without leaving a redirect)
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HistoryEdit
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | October 2021 | First public release |
IntroductionEdit
Classification and detection of materials is another interesting use case where Machine Learning (ML for short) opens the doors for promising developments related to smart edge devices. In this Technical Note (TN), different implementations of ML-based material classification applications are compared in terms of performance ac embedded solutions — intended as hardware platforms, software development tools,
ReferencesEdit
- Hadi Parastara, Geert van Kollenburgb, Yannick Weesepoelc, André van den Doelb, Lutgarde Buydensb, Jeroen Jansen, Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity, 2020
- N. Salamati, C. Fredembach, S. Susstrunk, Material Classification Using Color and NIR Images, 2009
- Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, 2019