View source for Publications:Boosting performance of the edge-based active contour model applied to phytoplankton images

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{{PublicationSetupTemplate|Author=Adas Gelzinis, Evaldas Vaiciukynas, Marija Bacauskiene, Antanas Verikas, Sigitas Sulcius, Ricardas Paskauskas, Irina Oleninaz
|PID=575340
|Name=Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Vaiciukynas, Evaldas (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (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), Intelligenta system (IS-lab) (3941));Sulcius, Sigitas (Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania);Paskauskas, Ricardas (Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania);Oleninaz, Irina (Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania)
|Title=Boosting performance of the edge-based active contour model applied to phytoplankton images
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
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|Status=
|Volume=
|Issue=
|HostPublication=Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics
|Conference=13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI2012), , November 20-22, Budapest, Hungary
|StartPage=273
|EndPage=277
|Year=2012
|Edition=
|Pages=
|City=Piscataway
|Publisher=IEEE Press
|Series=
|SeriesISSN=
|ISBN=978-1-4673-5210-9;978-1-4673-5205-5
|Urls=
|ISRN=
|DOI=http://dx.doi.org/10.1109/CINTI.2012.6496773
|ISI=000319991600046
|PMID=
|ScopusId=2-s2.0-84876939843
|NBN=urn:nbn:se:hh:diva-20074
|LocalId=
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|Keywords=Active contour model;Energy function;Contour detection;Image segmentation;Image preprocessing;Phytoplankton images
|Categories=Teknik och teknologier (2)
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|Abstract=<p>Automated contour detection for objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the core goal of this study. The speciesis known to cause harmful blooms in many estuarine and coastal environments. Active contour model (ACM)-based image segmentation is the approach adopted here as a potential solution. Currently, the main research in ACM area is highly focused ondevelopment of various energy functions having some physical intuition. This work, by contrast, advocates the idea of rich and diverse image preprocessing before segmentation. Advantage of the proposed preprocessing is demonstrated experimentally by comparing it to the six well known active contour techniques applied to the cell segmentation in microscopy imagery task.</p>
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|CreatedDate=2012-12-10
|PublicationDate=2012-12-10
|LastUpdated=2014-11-19
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:575340}}
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