Difference between revisions of "Publications:Phase congruency-based detection of circular objects applied to analysis of phytoplankton images"
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{{PublicationSetupTemplate|Author=Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Irina Olenina, Sergej Olenin, Evaldas Vaiciukynas | {{PublicationSetupTemplate|Author=Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Irina Olenina, Sergej Olenin, Evaldas Vaiciukynas | ||
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941)) (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania );Olenina, Irina (Klaipeda University, Klaipeda, Lithuania);Olenin, Sergej (Klaipeda University, Klaipeda, Lithuania );Vaiciukynas, Evaldas (Kaunas University of Technology, Kaunas, Lithuania) |
|Title=Phase congruency-based detection of circular objects applied to analysis of phytoplankton images | |Title=Phase congruency-based detection of circular objects applied to analysis of phytoplankton images | ||
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| − | |Abstract=<p>Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining | + | |Abstract=<p>Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. 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-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 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 classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved.</p> |
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| Title | Phase congruency-based detection of circular objects applied to analysis of phytoplankton images |
|---|---|
| Author | |
| Year | 2012 |
| PublicationType | Journal Paper |
| Journal | Pattern Recognition |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1016/j.patcog.2011.10.019 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:461849 |
| Abstract | Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. 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-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 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 classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved. |
