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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15780
Title: Multispectral images segmentation using new fuzzy cluster centroid modified
Authors: Mantilla, Luis
Yari Ramos, Yessenia Deysi
Keywords: Classification (of information);Fuzzy clustering;Cluster centroids;Clustering approach;Multispectral images;Probability informations;Satellite images;Segmentation analysis;Spatial relationships;Unsupervised classification;Image segmentation
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039985402&doi=10.1109%2fINTERCON.2017.8079724&partnerID=40&md5=ffee478f08343cf6495a05d0126ad124
Abstract: The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to inhomogeneity of characteristics. Furthermore, many algorithms such us FCM, PFCM, FCC, FWCM and modification aim to solve these problems by integrating spacial information. This process is carried through the analysis of the sample's neighborhood. This paper proposes the integration of the sample presence probability into a 'term' like form inside the existent model NFCC. This algorithm presents the basic steps for fuzzy clustering. With a middle variant that integrates the measure between each sample to all the centroids, this replaces the existent term by a new term. This new term integrates the spatial relationship between each sample of the multispectral image into a fitting term. The method is applied to multispectral images. Overall accuracy indicates that the term integrated to NFCC model decrease the overall cluster overlapping. © 2017 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15780
ISBN: urn:isbn:9781509063628
Appears in Collections:Artículos - Ciencia de la computación

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