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dc.contributor.author
Zanette, Damian Horacio
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dc.date.available
2020-01-10T21:42:53Z
dc.date.issued
2018-11
dc.identifier.citation
Zanette, Damian Horacio; Quantifying the complexity of black-and-white images; Public Library of Science; Plos One; 13; 11; 11-2018; 1-17
dc.identifier.issn
1932-6203
dc.identifier.uri
http://hdl.handle.net/11336/94438
dc.description.abstract
We propose a complexity measure for black-and-white (B/W) digital images, based on the detection of typical length scales in the depicted motifs. Complexity is associated with diversity in those length scales. In this sense, the proposed measure penalizes images where typical scales are limited to small lengths, of a few pixels –as in an image where gray levels are distributed at random– or to lengths similar to the image size –as when gray levels are ordered into a simple, broad pattern. We introduce a complexity index which captures the structural richness of images with a wide range of typical scales, and compare several images with each other on the basis of this index. Since the index provides an objective quantification of image complexity, it could be used as the counterpart of subjective visual complexity in experimental perception research. As an application of the complexity index, we build a “complexity map” for South-American topography, by analyzing a large B/W image that represents terrain elevation data in the continent. Results show that the complexity index is able to clearly reveal regions with intricate topographical features such as river drainage networks and fjord-like coasts. Although, for the sake of concreteness, our complexity measure is introduced for B/W images, the definition can be straightforwardly extended to any object that admits a mathematical representation as a function of one or more variables. Thus, the quantification of structural richness can be adapted to time signals and distributions of various kinds.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
COMPLEXITY MEASURE
dc.subject
IMAGE PROCESSING
dc.subject
COMPLEXITY INDEX
dc.subject
COMPLEXITY MAPS
dc.subject.classification
Otras Ciencias de la Computación e Información
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Quantifying the complexity of black-and-white images
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-15T17:57:16Z
dc.journal.volume
13
dc.journal.number
11
dc.journal.pagination
1-17
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Zanette, Damian Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Area de Investigación y Aplicaciones No Nucleares. Gerencia de Física (Centro Atómico Bariloche); Argentina
dc.journal.title
Plos One
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://dx.plos.org/10.1371/journal.pone.0207879
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pone.0207879
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