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Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).ORCID-id: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania .
Kaunas University of Technology, Kaunas, Lithuania .
Klaipeda University, Kaunas, Lithuania.
Vise andre og tillknytning
2012 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, nr 5, s. 6069-6077Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280 × 960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93.25% of the objects. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species. © 2011 Elsevier Ltd. All rights reserved.

sted, utgiver, år, opplag, sider
Amsterdam: Elsevier, 2012. Vol. 39, nr 5, s. 6069-6077
Emneord [en]
Image preprocessing, Phase congruency, Detection of circular objects, Stochastic optimization, Phytoplankton
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-16646DOI: 10.1016/j.eswa.2011.12.006ISI: 000301155300146Scopus ID: 2-s2.0-84855901953OAI: oai:DiVA.org:hh-16646DiVA, id: diva2:461861
Merknad

Funding: Grant (No. LEK-05/2010) from the Research Council of Lithuania.

Tilgjengelig fra: 2011-12-05 Laget: 2011-12-05 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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