Publications:Learning Accurate Active Contours
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| Title | Learning Accurate Active Contours |
|---|---|
| Author | |
| 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. |
