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
Learning Accurate Active Contours
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
2013 (English)In: Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I / [ed] Lazaros Iliadis, Harris Papadopoulos & Chrisina Jayne, Berlin Heidelberg: Springer Berlin/Heidelberg, 2013, Vol. 383, p. 396-405Conference paper, Published paper (Refereed)
Abstract [en]

Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments. We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images. © Springer-Verlag Berlin Heidelberg 2013.

Place, publisher, year, edition, pages
Berlin Heidelberg: Springer Berlin/Heidelberg, 2013. Vol. 383, p. 396-405
Series
Communications in Computer and Information Science, ISSN 1865-0929 ; 383
Keywords [en]
Active contour models, Energy function, Object detection, Image segmentation, Learning, Phytoplankton images
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-24042DOI: 10.1007/978-3-642-41013-0_41ISI: 000345333800041Scopus ID: 2-s2.0-84904605140ISBN: 978-3-642-41012-3 (print)ISBN: 978-3-642-41013-0 (electronic)OAI: oai:DiVA.org:hh-24042DiVA, id: diva2:668670
Conference
14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013
Note

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

Available from: 2013-12-02 Created: 2013-12-02 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

fulltext(5805 kB)232 downloads
File information
File name FULLTEXT01.pdfFile size 5805 kBChecksum SHA-512
ee82df3a98d5653ad0f32619bdeec29c8c57df51d85656a7c1bfff5b82d4153615620fe3dbe9d4af4a91d81c190d48081f6c84027c57ee301a760f124521028a
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Verikas, Antanas

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
CAISR - Center for Applied Intelligent Systems Research
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 232 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 130 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