Difference between revisions of "Publications:Categorizing cells in phytoplankton images"
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{{PublicationSetupTemplate|Author=Adas Gelzinis, Antanas Verikas, Marija Bacauskiene, Irina Olenina, Sergej Olenin | {{PublicationSetupTemplate|Author=Adas Gelzinis, Antanas Verikas, Marija Bacauskiene, Irina Olenina, Sergej Olenin | ||
|PID=438554 | |PID=438554 | ||
| − | |Name=Gelzinis, Adas (Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas | + | |Name=Gelzinis, Adas (Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, 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));Bacauskiene, Marija (Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania);Olenina, Irina (Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania);Olenin, Sergej (Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania) |
|Title=Categorizing cells in phytoplankton images | |Title=Categorizing cells in phytoplankton images | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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|CreatedDate=2011-09-03 | |CreatedDate=2011-09-03 | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:438554}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:438554}} | ||
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Latest revision as of 21:41, 30 September 2016
| Title | Categorizing cells in phytoplankton images |
|---|---|
| Author | Adas Gelzinis and Antanas Verikas and Marija Bacauskiene and Irina Olenina and Sergej Olenin |
| Year | 2011 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Recent Advances in Signal Processing, Computational Geometry and Systems Theory |
| Conference | The 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'11), Florence, Italy, August 23-25, 2011 |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:438554 |
| Abstract | This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. |
