dc.creatorSussner, P
dc.creatorRitter, G X
dc.date2000
dc.date2015-11-27T12:22:54Z
dc.date2015-11-27T12:22:54Z
dc.date.accessioned2018-03-29T00:54:48Z
dc.date.available2018-03-29T00:54:48Z
dc.identifierIeee Transactions On Image Processing : A Publication Of The Ieee Signal Processing Society. v. 9, n. 8, p. 1420-30, 2000.
dc.identifier1057-7149
dc.identifier10.1109/83.855436
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/18262978
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/194701
dc.identifier18262978
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1294934
dc.descriptionMethods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.
dc.description9
dc.description1420-30
dc.languageeng
dc.relationIeee Transactions On Image Processing : A Publication Of The Ieee Signal Processing Society
dc.relationIEEE Trans Image Process
dc.rightsfechado
dc.rights
dc.sourcePubMed
dc.titleRank-based Decompositions Of Morphological Templates.
dc.typeArtículos de revistas


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