Learning Accurate Active Contours
2013 (English)In: Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I / [ed] Lazaros Iliadis, Harris Papadopoulos and Chrisina Jayne, Berlin Heidelberg: Springer Berlin/Heidelberg, 2013, 396-405 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Berlin Heidelberg: Springer Berlin/Heidelberg, 2013. 396-405 p.
, Communications in Computer and Information Science, ISSN 1865-0929 ; 383
Active contour models, Energy function, Object detection, Image segmentation, Learning, Phytoplankton images
IdentifiersURN: urn:nbn:se:hh:diva-24042DOI: 10.1007/978-3-642-41013-0_41ScopusID: 2-s2.0-84904605140ISBN: 978-3-642-41012-3ISBN: 978-3-642-41013-0OAI: oai:DiVA.org:hh-24042DiVA: diva2:668670
14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16