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. 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 |
|---|---|---|
2008 | 10.1007/978-3-540-85567-5_38 | KES (3) |
Keywords | Field | DocType |
current labour intensive time,drinking water,micro-organism detection,fluorescein isothiocyanate,detection result,micro-organism oocysts,reliable detection,machine vision application,manual method,combining normarski differential interface,uv filter,consuming manual method,fluorescence microscopy,image processing,machine vision | Computer vision,Machine vision,Computer science,Image processing,Artificial intelligence,Nanometre,Microscopy | Conference |
Volume | ISSN | Citations |
5179 | 0302-9743 | 1 |
PageRank | References | Authors |
0.42 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hernando Fernandez-Canque | 1 | 8 | 5.07 |
| Sorin Hintea | 2 | 14 | 10.58 |
| Gabor Csipkes | 3 | 6 | 3.30 |
| Allan Pellow | 4 | 1 | 0.42 |
| Huw Smith | 5 | 2 | 0.91 |