Sökning: onr:"swepub:oai:DiVA.org:hh-16646" >
Automated image ana...
Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller
-
- Verikas, Antanas, 1951- (författare)
- Högskolan i Halmstad,Intelligenta system (IS-lab)
-
- Gelzinis, Adas (författare)
- Kaunas University of Technology, Kaunas, Lithuania
-
- Bacauskiene, Marija (författare)
- Kaunas University of Technology, Kaunas, Lithuania
-
visa fler...
-
- Olenina, Irina (författare)
- Klaipeda University, Kaunas, Lithuania
-
- Olenin, Sergej (författare)
- Klaipeda University, Klaipeda, Lithuania
-
- Vaiciukynas, Evaldas (författare)
- Kaunas University of Technology, Kaunas, Lithuania
-
visa färre...
-
(creator_code:org_t)
- Amsterdam : Elsevier, 2012
- 2012
- Engelska.
-
Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 6069-6077
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Image preprocessing
- Phase congruency
- Detection of circular objects
- Stochastic optimization
- Phytoplankton
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas