Difference between revisions of "Publications:Learning Accurate Active Contours"
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| − | |Name=Gelzinis, Adas (Kaunas University of Technology, Lithuania);Verikas, Antanas | + | |Name=Gelzinis, Adas (Kaunas University of Technology, Lithuania);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), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Bacauskiene, Marija (Kaunas University of Technology, Lithuania);Vaiciukynas, Evaldas (Kaunas University of Technology, Lithuania) |
|Title=Learning Accurate Active Contours | |Title=Learning Accurate Active Contours | ||
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Latest revision as of 21:41, 30 September 2016
| Title | Learning Accurate Active Contours |
|---|---|
| Author | Adas Gelzinis and Antanas Verikas and Marija Bacauskiene and Evaldas Vaiciukynas |
| Year | 2013 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Engineering Applications of Neural Networks : 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I |
| Conference | 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16 |
| DOI | http://dx.doi.org/10.1007/978-3-642-41013-0_41 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:668670 |
| Abstract | Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments.We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images. |
