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Texture feature performance for image segmentation

dc.contributor.authordu Buf, J. M. H.
dc.contributor.authorKardan, M.
dc.contributor.authorSpann, M.
dc.date.accessioned2015-11-04T12:04:40Z
dc.date.available2015-11-04T12:04:40Z
dc.date.issued1990
dc.description.abstractThis paper describes a comparative study of texture features, with particular emphasis on the applicability to unsupervised image segmentation. A benchmark test is introduced in which a set of 20 simple bipartite images, combining different stochastic textures separated by a stochastic boundary, is used for feature extraction and segmentation. The accuracy of the segmentation result, expressed in the mean boundary error, is used as an evaluation criterion. From the seven feature extraction methods tested, the Haralick, Laws and Unser methods gave best overall results. Results obtained also show that direct feature statistics such as the Bhattacharyya distance are not appropriate evaluation criteria if texture features are used for image segmentation. A small experiment on visual boundary tracking revealed that boundary error obtained here are similar to those obtained by machine segmentation. © 1990.
dc.identifier.issn0031-3203
dc.identifier.otherAUT: DUB00865;
dc.identifier.urihttp://hdl.handle.net/10400.1/6994
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.isbasedonP-008-XW2
dc.titleTexture feature performance for image segmentation
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage309
oaire.citation.issue3-4
oaire.citation.startPage291
oaire.citation.titlePattern Recognition
oaire.citation.volume23
person.familyNamedu Buf
person.givenNameHans
person.identifier.orcid0000-0002-4345-1237
person.identifier.ridM-5125-2013
person.identifier.scopus-author-id6604075916
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublicationcfad5636-2c77-4db0-a3a0-d7eb97ce6bee
relation.isAuthorOfPublication.latestForDiscoverycfad5636-2c77-4db0-a3a0-d7eb97ce6bee

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