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- Title
Machine vision system for automated spectroscopy.
- Authors
Ukwatta, Eranga; Samarabandu, Jagath; Hall, Mike
- Abstract
This paper describes a novel system based on the machine vision and machine learning techniques for fully automated, real-time identification of constituent elements in a sample specimen using laser-induced breakdown spectroscopy (LIBS) images. The proposed system is developed as a compact spectrum analyzer for rapid element detection using a commercially available video camera. We proposed a correlation-based pattern matching algorithm for analyzing single element spectra. However, the use of a high-speed laser and presence of numerous imperfections in the experimental setup require advanced techniques for analyzing multi-element spectra. We cast the element detection problem as a multi-label classification problem that uses support vector machines and artificial neural networks for multi-element classification. The proposed algorithms were evaluated using actual LIBS images. The machine learning approaches yielded correct identification of elements to an accuracy of 99%. Our system is useful in instances where a qualitative analysis is sufficient over a quantitative element analysis.
- Subjects
COMPUTER vision; CAMCORDERS; LASER-induced breakdown spectroscopy; MACHINE translating; SPECTRUM analyzers; QUANTITATIVE research
- Publication
Machine Vision & Applications, 2012, Vol 23, Issue 1, p111
- ISSN
0932-8092
- Publication type
Article
- DOI
10.1007/s00138-011-0338-8