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Article: Scale-based detection of corners of planar curves

TitleScale-based detection of corners of planar curves
Authors
KeywordsProbability - Random Processes
Issue Date1992
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, 1992, v. 14 n. 4, p. 430-449 How to Cite?
AbstractA technique for detecting and localizing corners of planar curves is proposed. The technique is based on Gaussian scale space, which consists of the maxima of absolute curvature of the boundary function presented at all scales. The scale space of isolated simple and double corners is first analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners. The analysis shows that the resulting scale space contains line patterns that either persist, terminate, or merge with a neighboring line. Next, the scale space is transformed into a tree that provides simple but concise representation of corners at multiple scales. Finally, a multiple-scale corner detection scheme is developed using a coarse-to-fine tree parsing technique. The parsing scheme is based on a stability criterion that states that the presence of a corner must concur with a curvature maximum observable at a majority of scales. Experiments were performed to show that the scale space corner detector is reliable for objects with multiple-size features and noisy boundaries and compares favorably with other corner detectors tested.
Persistent Identifierhttp://hdl.handle.net/10722/65541
ISSN
2015 Impact Factor: 6.077
2015 SCImago Journal Rankings: 7.653
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorRattarangsi, Anothaien_HK
dc.contributor.authorChin, Roland Ten_HK
dc.date.accessioned2010-08-31T07:15:14Z-
dc.date.available2010-08-31T07:15:14Z-
dc.date.issued1992en_HK
dc.identifier.citationIeee Transactions On Pattern Analysis And Machine Intelligence, 1992, v. 14 n. 4, p. 430-449en_HK
dc.identifier.issn0162-8828en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65541-
dc.description.abstractA technique for detecting and localizing corners of planar curves is proposed. The technique is based on Gaussian scale space, which consists of the maxima of absolute curvature of the boundary function presented at all scales. The scale space of isolated simple and double corners is first analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners. The analysis shows that the resulting scale space contains line patterns that either persist, terminate, or merge with a neighboring line. Next, the scale space is transformed into a tree that provides simple but concise representation of corners at multiple scales. Finally, a multiple-scale corner detection scheme is developed using a coarse-to-fine tree parsing technique. The parsing scheme is based on a stability criterion that states that the presence of a corner must concur with a curvature maximum observable at a majority of scales. Experiments were performed to show that the scale space corner detector is reliable for objects with multiple-size features and noisy boundaries and compares favorably with other corner detectors tested.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.subjectProbability - Random Processesen_HK
dc.titleScale-based detection of corners of planar 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.doi10.1109/34.126805en_HK
dc.identifier.scopuseid_2-s2.0-0026852844en_HK
dc.identifier.volume14en_HK
dc.identifier.issue4en_HK
dc.identifier.spage430en_HK
dc.identifier.epage449en_HK
dc.identifier.isiWOS:A1992HL19300003-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridRattarangsi, Anothai=6506172878en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK

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