Discrete morphological size distributions and densities: Estimation techniques and applications

Sivakumar, K; Goutsias, J

dc.contributor.author	Sivakumar, K	en
dc.contributor.author	Goutsias, J	en
dc.date.accessioned	2014-03-01T01:45:55Z
dc.date.available	2014-03-01T01:45:55Z
dc.date.issued	1997	en
dc.identifier.issn	10179909	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/24797
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0002350524&partnerID=40&md5=cd8a4fe76dc939db923f2f21d46b79d6	en
dc.title	Discrete morphological size distributions and densities: Estimation techniques and applications	en
heal.type	journalArticle	en
heal.publicationDate	1997	en
heal.abstract	Morphological size distributions and densities are frequently used as descriptors of granularity or texture within an image. They have been successfully employed in a number of image processing and analysis tasks, including shape analysis, multiscale shape representation, texture classification, and noise filtering. In most cases however it is not possible to analytically compute these quantities. In this paper, we study the problem of estimating the (discrete) morphological size distribution and density of random images, by means of empirical as well as Monte Carlo estimators. Theoretical and experimental results demonstrate clear superiority of the Monte Carlo estimation approach. Examples illustrate the usefulness of the proposed estimators in traditional image processing and analysis problems. © 1997 SPIE and IS&T.	en
heal.journalName	Journal of Electronic Imaging	en
dc.identifier.volume	6	en
dc.identifier.issue	1	en
dc.identifier.spage	31	en
dc.identifier.epage	53	en