Name
Abstract | ||
|---|---|---|
The work presented in this paper uses a novel Machine Vision application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. The machine vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/IDT-2009-0050 | Intelligent Decision Technologies |
Keywords | Field | DocType |
current labour intensive time,micro-organism detection,fluorescein isothiocyanate,detection result,micro-organism oocysts,cryptosporidium micro-organism,water-borne micro-organism,reliable detection,manual method,combining normarski differential interface,machine vision application,consuming manual method,machine vision | Computer vision,Machine vision,Computer science,Image processing,Real-time computing,Artificial intelligence | Journal |
Volume | Issue | ISSN |
3 | 2 | 1872-4981 |
Citations | PageRank | References |
1 | 0.48 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hernando Fernandez-Canque | 1 | 8 | 5.07 |
| Sorin Hintea | 2 | 14 | 10.58 |
| Gabor Csipkes | 3 | 6 | 3.30 |
| Sorin Bota | 4 | 1 | 0.48 |
| Huw Smith | 5 | 2 | 0.91 |