Shape analysis using multiscale hough transform statistics

dc.contributor.authorRamos, Lucas Alexandre [UNESP]
dc.contributor.authorde Souza, Gustavo Botelho
dc.contributor.authorMarana, Aparecido Nilceu [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2018-12-11T17:29:24Z
dc.date.available2018-12-11T17:29:24Z
dc.date.issued2015-01-01
dc.description.abstractWith the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose MHTS (Multiscale Hough Transform Statistics), a multiscale version of the shape description method called HTS (Hough Transform Statistics). Likewise HTS, MHTS uses statistics from the Hough Transform to characterize the shape of objects or regions in digital images. Experiments carried out on MPEG-7 CE-1 (Part B) shape database show that MHTS is better than the original HTS, and presents superior precision–recall results than some well-known shape description methods, such as: Tensor Scale, Multiscale Fractal Dimension, Fourier, and Contour Salience. Besides, when using the multiscale separability criterion, MHTS is also superior to Zernike Moments and Beam Angle Statistics (BAS) methods. The linear complexity of the HTS algorithm was preserved in this new multiscale version, making MHTS even more appropriate than BAS method for shape analysis in high-resolution image retrieval tasks when very large databases are used.en
dc.description.affiliationFaculty of Sciences UNESP
dc.description.affiliationDepartment of Computing UFSCar
dc.description.affiliationUnespFaculty of Sciences UNESP
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2014/10611-0
dc.format.extent452-459
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-25751-8_54
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 452-459.
dc.identifier.doi10.1007/978-3-319-25751-8_54
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84983605187
dc.identifier.urihttp://hdl.handle.net/11449/178229
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.titleShape analysis using multiscale hough transform statisticsen
dc.typeTrabalho apresentado em evento
unesp.author.lattes6027713750942689[3]
unesp.author.orcid0000-0003-4861-7061[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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