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Categorizing cells ...
Categorizing cells in phytoplankton images
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- Gelzinis, Adas (author)
- Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania
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- Verikas, Antanas (author)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS)
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- Bacauskiene, Marija (author)
- Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania
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- Olenina, Irina (author)
- Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania
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- Olenin, Sergej (author)
- Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania
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(creator_code:org_t)
- Athens : World Scientific and Engineering Academy and Society, 2011
- 2011
- English.
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In: Recent Advances in Signal Processing, Computational Geometry and Systems Theory. - Athens : World Scientific and Engineering Academy and Society. - 9781618040275 - 1618040278 ; , s. 82-87
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Phase congruency
- Detection of circular objects
- SVM
- Random forests
- Stochastic optimization
- Phytoplankton
- Information technology
- Informationsteknik
Publication and Content Type
- ref (subject category)
- kon (subject category)
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