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Conference Paper: Curvature scale space corner detector with adaptive threshold and dynamic region of support

TitleCurvature scale space corner detector with adaptive threshold and dynamic region of support
Authors
KeywordsComputers
Artificial intelligence
Issue Date2004
PublisherIEEE, Computer Society.
Citation
Proceedings - International Conference On Pattern Recognition, 2004, v. 2, p. 791-794 How to Cite?
AbstractCorners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on Curvature Scale Space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features.
Persistent Identifierhttp://hdl.handle.net/10722/46475
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorHe, XCen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2007-10-30T06:50:40Z-
dc.date.available2007-10-30T06:50:40Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings - International Conference On Pattern Recognition, 2004, v. 2, p. 791-794en_HK
dc.identifier.issn1051-4651en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46475-
dc.description.abstractCorners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on Curvature Scale Space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features.en_HK
dc.format.extent890791 bytes-
dc.format.extent484226 bytes-
dc.format.extent10863 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofProceedings - International Conference on Pattern Recognitionen_HK
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleCurvature scale space corner detector with adaptive threshold and dynamic region of supporten_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1051-4651&volume=2&spage=791&epage=794&date=2004&atitle=Curvature+scale+space+corner+detector+with+adaptive+threshold+and+dynamic+region+of+supporten_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICPR.2004.1334377en_HK
dc.identifier.scopuseid_2-s2.0-10044244736en_HK
dc.identifier.hkuros91677-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-10044244736&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2en_HK
dc.identifier.spage791en_HK
dc.identifier.epage794en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHe, XC=54781404800en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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