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Article: On the detection of dominant points on digital curves

TitleOn the detection of dominant points on digital curves
Authors
KeywordsComputer Systems, Digital--Parallel Processing
Pattern Recognition
Signal Filtering and Prediction
Issue Date1989
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tpami
Citation
Ieee Transactions On Pattern Analysis And Machine Intelligence, 1989, v. 11 n. 8, p. 859-872 How to Cite?
AbstractA parallel algorithm is presented for detecting dominant points on a digital closed curve. The procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve. The procedure first determines the region of support for each point based on its local properties, then computes measures of relative significance (e.g., curvature) of each point, and finally detects dominant points by a process of nonmaximum suppression. This procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but also on the precise determination of the region of support. This solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms. The inherent nature of scale-space filtering in the procedure is addressed, and the performance of the procedure is compared to those of several other dominant point detection algorithms, using a number of examples.
Persistent Identifierhttp://hdl.handle.net/10722/65542
ISSN
2015 Impact Factor: 6.077
2015 SCImago Journal Rankings: 7.653
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTeh, ChoHuaken_HK
dc.contributor.authorChin, Roland Ten_HK
dc.date.accessioned2010-08-31T07:15:14Z-
dc.date.available2010-08-31T07:15:14Z-
dc.date.issued1989en_HK
dc.identifier.citationIeee Transactions On Pattern Analysis And Machine Intelligence, 1989, v. 11 n. 8, p. 859-872en_HK
dc.identifier.issn0162-8828en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65542-
dc.description.abstractA parallel algorithm is presented for detecting dominant points on a digital closed curve. The procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve. The procedure first determines the region of support for each point based on its local properties, then computes measures of relative significance (e.g., curvature) of each point, and finally detects dominant points by a process of nonmaximum suppression. This procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but also on the precise determination of the region of support. This solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms. The inherent nature of scale-space filtering in the procedure is addressed, and the performance of the procedure is compared to those of several other dominant point detection algorithms, using a number of examples.en_HK
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tpamien_HK
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_HK
dc.subjectComputer Systems, Digital--Parallel Processingen_HK
dc.subjectPattern Recognitionen_HK
dc.subjectSignal Filtering and Predictionen_HK
dc.titleOn the detection of dominant points on digital curvesen_HK
dc.typeArticleen_HK
dc.identifier.emailChin, Roland T: rchin@hku.hken_HK
dc.identifier.authorityChin, Roland T=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.doi10.1109/34.31447en_HK
dc.identifier.scopuseid_2-s2.0-0024715150en_HK
dc.identifier.volume11en_HK
dc.identifier.issue8en_HK
dc.identifier.spage859en_HK
dc.identifier.epage872en_HK
dc.identifier.isiWOS:A1989AH07900006-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTeh, ChoHuak=7004389493en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK

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