Self-referred approach to lacunarity

dc.contributor.authorRodrigues, Erbe P
dc.contributor.authorBarbosa, Marconi
dc.contributor.authorda F. Costa, Luciano
dc.date.accessioned2015-12-10T21:54:50Z
dc.date.issued2005
dc.date.updated2015-12-09T07:29:15Z
dc.description.abstractThis paper describes an approach to lacunarity which adopts the pattern under analysis as the reference for the sliding window procedure. The superiority of such a scheme with respect to more traditional methodologies, especially when dealing with finite-size objects, is established and illustrated through applications to diffusion limited aggregation pattern characterization. It is also shown that, given the enhanced accuracy and sensitivity of this scheme, the shape of the window becomes an important parameter, with advantage for circular windows.
dc.identifier.issn1539-3755
dc.identifier.urihttp://hdl.handle.net/1885/39109
dc.publisherAmerican Physical Society
dc.sourcePhysical Review E-Statistical, Nonlinear and Soft Matter Physics
dc.subjectKeywords: Finite-size objects; Sliding window procedure; Agglomeration; Diffusion; Sensitivity analysis; Lacquers
dc.titleSelf-referred approach to lacunarity
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage6
local.bibliographicCitation.startpage016707 1
local.contributor.affiliationRodrigues, Erbe P, University of Sao Paulo
local.contributor.affiliationBarbosa, Marconi, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationda F. Costa, Luciano, University of Sao Paulo
local.contributor.authoruidBarbosa, Marconi, u1820479
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.absfor010202 - Biological Mathematics
local.identifier.absfor060102 - Bioinformatics
local.identifier.absseo970111 - Expanding Knowledge in the Medical and Health Sciences
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
local.identifier.ariespublicationu8611701xPUB171
local.identifier.citationvolume72
local.identifier.doi10.1103/PhysRevE.72.016707
local.identifier.scopusID2-s2.0-27244457061
local.type.statusPublished Version

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