Difference between revisions of "Publications:Detecting P. minimum cells in phytoplankton images"

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|Name=Gelzinis, Adas (Kaunas University of Technology, Lithuania);Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Bacauskiene, Marija (Kaunas University of Technology, Lithuania);Olenina, Irina (Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania);Olenin, Sergej (dCoastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania)
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|Name=Gelzinis, Adas (Kaunas University of Technology, Lithuania);Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Bacauskiene, Marija (Kaunas University of Technology, Lithuania);Olenina, Irina (Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania);Olenin, Sergej (dCoastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania)
 
|Title=Detecting P. minimum cells in phytoplankton images
 
|Title=Detecting P. minimum cells in phytoplankton images
 
|PublicationType=Conference Paper
 
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|CreatedDate=2011-08-25
 
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|PublicationDate=2011-09-15
 
|PublicationDate=2011-09-15
|LastUpdated=2012-12-20
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Title Detecting P. minimum cells in phytoplankton images
Author
Year 2011
PublicationType Conference Paper
Journal
HostPublication Electrical and Control Technologies : proceedings of the 6th international conference on Electrical and Control Technologies ECT 2011 / Kaunas University of Technology, IFAC Committee of National Lithuanian Organisation
Conference The 6th international conference on Electrical and Control Technologies ECT 2011, May 5-6, 2011, Kaunas, Lithuania
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:436821
Abstract

This article is concerned with detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), - which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280x960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93,25% of the objects. The results are rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species.