A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes

Detalhes bibliográficos
Autor(a) principal: LOPES, Guilherme
Data de Publicação: 2019
Outros Autores: HORVATH, M., GIRALDI, G. A.
Tipo de documento: Artigo
Título da fonte: Biblioteca Digital de Teses e Dissertações da FEI
Texto Completo: https://repositorio.fei.edu.br/handle/FEI/1010
https://doi.org/10.1016/j.image.2019.03.007
Resumo: 75
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spelling LOPES, GuilhermeHORVATH, M.GIRALDI, G. A.LOPES, Guilherme2019-08-17T20:00:30Z2019-08-17T20:00:30Z2019LOPES, Guilherme; HORVATH, M.; GIRALDI, G. A.; LOPES, Guilherme. A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes. Signal Processing: Image Communication, v. 75, p. 44-54, 2019.0923-5965https://repositorio.fei.edu.br/handle/FEI/101010.1016/j.image.2019.03.007https://doi.org/10.1016/j.image.2019.03.007Signal Processing: Image CommunicationA strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article754454In recent decades, methods to find invariant points in digital images, called fiducial points, have gained greatattention, mainly due to the demands of several applications in computer vision and image processing, suchas the geometric matching of global structures, objects or specific regions. Among the most well knownapproaches are algorithms like SIFT, HOG, SURF, and their variations as A-SIFT, PCA-SIFT, surrounded bymany others. Though the number of researches demonstrating the efficiency of such methods is undoubtedlyenormous, the vast majority compares their performances only on pairs of images with little changes in viewperspectives, objects or specific regions of the scenes. Thereby, the study of this type of technique under largeviewpoint changes, called here LVC, has received little attention from the researchers. On the other hand, withthe aim of filtering points of interest, most techniques have used the traditional extensive statistics. However,methods for image processing based on a new type of statistics, called non-extensive statistics, have shownto be efficient in several applications. In this paper, we present a new method, called-SIFT, based on thenon-extensive Tsallis statistics, to find fiducial points in a sequence of frames of videos under large viewpointschanges. We experimentally show the efficiency of the proposed method in video databases and propose newmeasurement metrics for this type of algorithm.SIFTTsallis statistics-Gaussian functionImage matchingImage registrationinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da FEIinstname:Centro Universitário da Fundação Educacional Inaciana (FEI)instacron:FEIFEI/10102019-10-23 14:01:01.559Biblioteca Digital de Teses e Dissertaçõeshttp://sofia.fei.edu.br/pergamum/biblioteca/PRI
dc.title.pt_BR.fl_str_mv A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
title A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
spellingShingle A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
LOPES, Guilherme
SIFT
Tsallis statistics
-Gaussian function
Image matching
Image registration
title_short A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
title_full A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
title_fullStr A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
title_full_unstemmed A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
title_sort A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes
author LOPES, Guilherme
author_facet LOPES, Guilherme
HORVATH, M.
GIRALDI, G. A.
author_role author
author2 HORVATH, M.
GIRALDI, G. A.
author2_role author
author
dc.contributor.author.fl_str_mv LOPES, Guilherme
HORVATH, M.
GIRALDI, G. A.
LOPES, Guilherme
dc.subject.eng.fl_str_mv SIFT
Tsallis statistics
-Gaussian function
Image matching
Image registration
topic SIFT
Tsallis statistics
-Gaussian function
Image matching
Image registration
description 75
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-08-17T20:00:30Z
dc.date.available.fl_str_mv 2019-08-17T20:00:30Z
dc.date.issued.fl_str_mv 2019
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv LOPES, Guilherme; HORVATH, M.; GIRALDI, G. A.; LOPES, Guilherme. A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes. Signal Processing: Image Communication, v. 75, p. 44-54, 2019.
dc.identifier.uri.fl_str_mv https://repositorio.fei.edu.br/handle/FEI/1010
dc.identifier.issn.none.fl_str_mv 0923-5965
dc.identifier.doi.none.fl_str_mv 10.1016/j.image.2019.03.007
dc.identifier.url.none.fl_str_mv https://doi.org/10.1016/j.image.2019.03.007
identifier_str_mv LOPES, Guilherme; HORVATH, M.; GIRALDI, G. A.; LOPES, Guilherme. A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes. Signal Processing: Image Communication, v. 75, p. 44-54, 2019.
0923-5965
10.1016/j.image.2019.03.007
url https://repositorio.fei.edu.br/handle/FEI/1010
https://doi.org/10.1016/j.image.2019.03.007
dc.relation.ispartof.none.fl_str_mv Signal Processing: Image Communication
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Biblioteca Digital de Teses e Dissertações da FEI
instname:Centro Universitário da Fundação Educacional Inaciana (FEI)
instacron:FEI
instname_str Centro Universitário da Fundação Educacional Inaciana (FEI)
instacron_str FEI
institution FEI
reponame_str Biblioteca Digital de Teses e Dissertações da FEI
collection Biblioteca Digital de Teses e Dissertações da FEI
repository.name.fl_str_mv
repository.mail.fl_str_mv
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