HTS and HTSn: New shape descriptors based on Hough transform statistics

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorSouza, Gustavo Botelho de [UNESP]
dc.creatorMarana, Aparecido Nilceu [UNESP]
dc.date2015-03-18T15:53:24Z
dc.date2015-03-18T15:53:24Z
dc.date2014-10-01
dc.date.accessioned2023-09-09T11:05:38Z
dc.date.available2023-09-09T11:05:38Z
dc.identifierhttp://dx.doi.org/10.1016/j.cviu.2014.06.010
dc.identifierComputer Vision And Image Understanding. San Diego: Academic Press Inc Elsevier Science, v. 127, p. 43-56, 2014.
dc.identifier1077-3142
dc.identifierhttp://hdl.handle.net/11449/116495
dc.identifier10.1016/j.cviu.2014.06.010
dc.identifierWOS:000340692000004
dc.identifier6027713750942689
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8765980
dc.descriptionWith 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 a new shape description method, called Hough Transform Statistics (HTS), which uses statistics from the Hough space to characterize the shape of objects or regions in digital images. A modified version of this method, called Hough Transform Statistics neighborhood (HTSn), is also presented. Experiments carried out on three popular public image databases showed that the HTS and HTSn descriptors are robust, since they presented precision-recall results much better than several other well-known shape description methods. When compared to Beam Angle Statistics (BAS) method, a shape description method that inspired their development, both the HTS and the HTSn methods presented inferior results regarding the precision-recall criterion, but superior results in the processing time and multiscale separability criteria. The linear complexity of the HTS and the HTSn algorithms, in contrast to BAS, make them more appropriate for shape analysis in high-resolution image retrieval tasks when very large databases are used, which are very common nowadays. (C) 2014 Elsevier Inc. All rights reserved.
dc.descriptionUniv Estadual Paulista, UNESP, Fac Sci, Dept Comp, BR-17033360 Sao Paulo, Brazil
dc.descriptionUniv Estadual Paulista, UNESP, Fac Sci, Dept Comp, BR-17033360 Sao Paulo, Brazil
dc.format43-56
dc.languageeng
dc.publisherElsevier B.V.
dc.relationComputer Vision And Image Understanding
dc.relation2.391
dc.relation0,717
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectHTS
dc.subjectHTSn
dc.subjectImage analysis
dc.subjectShape analysis
dc.subjectHough transform
dc.subjectContent-based image retrieval
dc.subjectOptical character recognition
dc.titleHTS and HTSn: New shape descriptors based on Hough transform statistics
dc.typeArtigo


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