hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Categorizing cells in phytoplankton images
Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania.
Show others and affiliations
2011 (English)In: Recent Advances in Signal Processing, Computational Geometry and Systems Theory / [ed] Myriam Lazard ... [et al.], Athens: World Scientific and Engineering Academy and Society, 2011, p. 82-87Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Athens: World Scientific and Engineering Academy and Society, 2011. p. 82-87
Keywords [en]
Phase congruency, Detection of circular objects, SVM, Random forests, Stochastic optimization, Phytoplankton
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-16104Scopus ID: 2-s2.0-82655164347ISBN: 978-161804027-5 ISBN: 1618040278 OAI: oai:DiVA.org:hh-16104DiVA, id: diva2:438554
Conference
The 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'11), Florence, Italy, August 23-25, 2011
Available from: 2011-09-03 Created: 2011-09-03 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Verikas, Antanas

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
Halmstad Embedded and Intelligent Systems Research (EIS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 183 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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