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.
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Fernandez-Canque, H., Hintea, S., Csipkes, G., Pellow, A., Smith, H. (2008). Machine Vision Application to the Detection of Micro-organism in Drinking Water. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_38
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DOI: https://doi.org/10.1007/978-3-540-85567-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85566-8
Online ISBN: 978-3-540-85567-5
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