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A noise tolerant watershed transformation with viscous force for seeded image segmentation

Yang, Di; Gould, Stephen; Hutter, Marcus

Description

The watershed transform was proposed as a novel method for image segmentation over 30 years ago. Today it is still used as an elementary step in many powerful segmentation procedures. The watershed transform constitutes one of the main concepts of mathematical morphology as an important region-based image segmentation approach. However, the original watershed transform is highly sensitive to noise and is incapable of detecting objects with broken edges. Consequently its adoption in domains...[Show more]

dc.contributor.authorYang, Di
dc.contributor.authorGould, Stephen
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-17T04:21:19Z
dc.date.available2015-08-17T04:21:19Z
dc.identifier.isbn978-3-642-37330-5
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/14732
dc.description.abstractThe watershed transform was proposed as a novel method for image segmentation over 30 years ago. Today it is still used as an elementary step in many powerful segmentation procedures. The watershed transform constitutes one of the main concepts of mathematical morphology as an important region-based image segmentation approach. However, the original watershed transform is highly sensitive to noise and is incapable of detecting objects with broken edges. Consequently its adoption in domains where imaging is subject to high noise is limited. By incorporating a high-order energy term into the original watershed transform, we proposed the viscous force watershed transform, which is more immune to noise and able to detect objects with broken edges.
dc.publisherSpringer Verlag
dc.relation.ispartofComputer vision-- ACCV 2012 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised selected papers. Part I
dc.rights© Springer-Verlag Berlin Heidelberg 2013
dc.titleA noise tolerant watershed transformation with viscous force for seeded image segmentation
dc.typeConference paper
local.identifier.citationvolume7724
dc.date.issued2013
local.type.statusPublished Version
local.contributor.affiliationYang, D., Research School of Computer Science, The Australian National University
local.contributor.affiliationGould, S., Research School of Computer Science, The Australian National University
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage775
local.bibliographicCitation.lastpage789
local.identifier.doi10.1007/978-3-642-37331-2_58
CollectionsANU Research Publications

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