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dc.contributor.author
Schmaedech, Diego Martins
dc.contributor.author
Galván Josa, Víctor Martín
dc.contributor.author
Castellano, Gustavo Eugenio
dc.contributor.author
Costa, José A. T. Borges Da
dc.date.available
2017-01-30T20:16:56Z
dc.date.issued
2013-10
dc.identifier.citation
Schmaedech, Diego Martins; Galván Josa, Víctor Martín; Castellano, Gustavo Eugenio; Costa, José A. T. Borges Da; Phase Classification by Mean Shift Clustering of Multispectral Materials Images; Cambridge University Press; Microscopy & Microanalysis; 19; 5; 10-2013; 1266-1275
dc.identifier.issn
1431-9276
dc.identifier.uri
http://hdl.handle.net/11336/12200
dc.description.abstract
A mean-shift clustering (MSC) algorithm is introduced as a valuable alternative to perform materials phase classification from multispectral images. As opposed to other multivariate statistical techniques, such as factor analysis or principal component analysis (PCA), clustering techniques directly assign a class label to each pixel, so that their outputs are phase segmented images, i.e., there is no need for an additional segmentation algorithm. On the other hand, as compared to other clustering procedures and classification methods, such as segmentation by thresholding of multiple spectral components, MSC has the advantages of not requiring previous knowledge of the number of data clusters and not assuming any shape for these clusters, i.e., neither the number nor the composition of the phases must be previously known. This makes MSC a particularly useful tool for exploratory research, assisting phase identification of unknown samples. Visualization and interpretation of the results are also simplified, since the information content of the output image does not depend on the particular choice of the content of the color channels. We applied MSC to the analysis of two sets of X-ray maps acquired in scanning electron microscopes equipped with energy-dispersive detection systems. Our results indicate that MSC is capable of detecting additional phases, not clearly identified through PCA or multiple thresholding, with a very low empirical reject rate.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Cambridge University Press
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Phase Classification
dc.subject
X-Ray Maps
dc.subject
Sem
dc.subject
Image Segmentation
dc.subject
Mean Shift Clustering
dc.subject.classification
Física Atómica, Molecular y Química
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Phase Classification by Mean Shift Clustering of Multispectral Materials 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
2016-12-06T16:31:19Z
dc.journal.volume
19
dc.journal.number
5
dc.journal.pagination
1266-1275
dc.journal.pais
Reino Unido
dc.journal.ciudad
Cambridge
dc.description.fil
Fil: Schmaedech, Diego Martins. Universidade Federal de Santa Maria. Programa de Pós-Graduação em Informática; Brasil
dc.description.fil
Fil: Galván Josa, Víctor Martín. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física. Sección Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Física "Enrique Gaviola"; Argentina
dc.description.fil
Fil: Castellano, Gustavo Eugenio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física. Sección Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Física "Enrique Gaviola"; Argentina
dc.description.fil
Fil: Costa, José A. T. Borges Da. Universidade Federal de Santa Maria. Departamento de Física; Brasil
dc.journal.title
Microscopy & Microanalysis
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/microscopy-and-microanalysis/article/div-classtitlephase-classification-by-mean-shift-clustering-of-multispectral-materials-imagesdiv/6B1E7976F84FC880C72F8EC3AC1AE66A
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1017/S1431927613001888
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