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Conference Paper: An edge detection algorithm based on rectangular gaussian kernels for machine vision applications

TitleAn edge detection algorithm based on rectangular gaussian kernels for machine vision applications
Authors
KeywordsDirectional convolution
Edge detection
Gaussian kernel
Machine vision applications
Rectangular kernel
Issue Date2009
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Proceedings Of Spie - The International Society For Optical Engineering, 2009, v. 7251 How to Cite?
AbstractIn this paper, we develop rectangular Gaussian kernels, i.e. all the rotated versions of the first order partial derivatives of the 2D nonsymmetrical Gaussian functions, which are used to convolve with the test images for edge extraction. By using rectangular kernels, one can have greater flexibility to smooth high frequency noise while keeping the high frequency edge details. When using larger kernels for edge detection, one can smooth more high frequency noise at the expense of edge details. Rectangular kernels allow us to smooth more noise along one direction and detect better edge details along the other direction, which improve the overall edge detection results especially when detecting line pattern edges. Here we propose two new approaches in using rectangular Gaussian kernels, namely the pattern-matching method and the quadratic method. The magnitude and directional edge from these two methods are computed based on the convolution results of the small neighborhood of the edge point with the rectangular Gaussian kernels along different directions. © 2009 SPIE.
DescriptionImage Processing: Machine Vision Applications, volume 7251 of Proceedings of the SPIE
Persistent Identifierhttp://hdl.handle.net/10722/61964
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorDeng, Fen_HK
dc.contributor.authorFung, KSMen_HK
dc.contributor.authorDeng, Jen_HK
dc.contributor.authorLam, EYen_HK
dc.date.accessioned2010-07-13T03:51:07Z-
dc.date.available2010-07-13T03:51:07Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings Of Spie - The International Society For Optical Engineering, 2009, v. 7251en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/61964-
dc.descriptionImage Processing: Machine Vision Applications, volume 7251 of Proceedings of the SPIEen_HK
dc.description.abstractIn this paper, we develop rectangular Gaussian kernels, i.e. all the rotated versions of the first order partial derivatives of the 2D nonsymmetrical Gaussian functions, which are used to convolve with the test images for edge extraction. By using rectangular kernels, one can have greater flexibility to smooth high frequency noise while keeping the high frequency edge details. When using larger kernels for edge detection, one can smooth more high frequency noise at the expense of edge details. Rectangular kernels allow us to smooth more noise along one direction and detect better edge details along the other direction, which improve the overall edge detection results especially when detecting line pattern edges. Here we propose two new approaches in using rectangular Gaussian kernels, namely the pattern-matching method and the quadratic method. The magnitude and directional edge from these two methods are computed based on the convolution results of the small neighborhood of the edge point with the rectangular Gaussian kernels along different directions. © 2009 SPIE.en_HK
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_HK
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_HK
dc.subjectDirectional convolutionen_HK
dc.subjectEdge detectionen_HK
dc.subjectGaussian kernelen_HK
dc.subjectMachine vision applicationsen_HK
dc.subjectRectangular kernelen_HK
dc.titleAn edge detection algorithm based on rectangular gaussian kernels for machine vision applicationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.805241en_HK
dc.identifier.scopuseid_2-s2.0-65949112043en_HK
dc.identifier.hkuros158740en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-65949112043&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7251en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridDeng, F=35619947800en_HK
dc.identifier.scopusauthoridFung, KSM=8627247700en_HK
dc.identifier.scopusauthoridDeng, J=35620061100en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK

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