Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00138-011-0338-8 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Laser-induced breakdown spectroscopy,LIBS,Element identification,Machine vision,SVM,Multi-label classification | String searching algorithm,Computer vision,Laser-induced breakdown spectroscopy,Pattern recognition,Machine vision,Computer science,Support vector machine,Multi-label classification,Artificial intelligence,Video camera,Artificial neural network,Spectrum analyzer | Journal |
Volume | Issue | ISSN |
23 | 1 | 0932-8092 |
Citations | PageRank | References |
2 | 0.51 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eranga Ukwatta | 1 | 154 | 18.10 |
| Jagath Samarabandu | 2 | 133 | 20.50 |
| Mike Hall | 3 | 2 | 0.51 |