hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Boosting performance of the edge-based active contour model applied to phytoplankton images
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Show others and affiliations
2012 (English)In: Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics, Piscataway, NJ: IEEE Press, 2012, 273-277 p.Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2012 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2012. 273-277 p.
Keyword [en]
Active contour model, Energy function, Contour detection, Image segmentation, Image preprocessing, Phytoplankton images
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-20074DOI: 10.1109/CINTI.2012.6496773ISI: 000319991600046Scopus ID: 2-s2.0-84876939843ISBN: 978-1-4673-5206-2 (electronic)ISBN: 978-1-4673-5205-5 (print)ISBN: 978-1-4673-5210-9 (electronic)OAI: oai:DiVA.org:hh-20074DiVA: diva2:575340
Conference
13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI2012), November 20-22, 2012, Budapest, Hungary
Note

This research was funded by a grant (No. LEK-09/2012) from the Research Council of Lithuania.

Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2017-04-24Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
Intelligent systems (IS-lab)
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 188 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf