Boosting performance of the edge-based active contour model applied to phytoplankton imagesShow others and affiliations
2012 (English)In: Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics, Piscataway, NJ: IEEE Press, 2012, p. 273-277Conference 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. p. 273-277
Keywords [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, id: 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.
2012-12-102012-12-102018-01-12Bibliographically approved