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Conference Paper: A 2D multirate Bayesian framework for multiscale feature detection

TitleA 2D multirate Bayesian framework for multiscale feature detection
Authors
Keywords2D Feature Detection
Bayes Classifier
Edge Detection
Filter Bank
Multirate
Multiscale
Scale-Space Analysis
Issue Date1996
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, 1996, v. 2825, p. 330-341 How to Cite?
AbstractThis paper presents a novel methodology for designing a 2D multiscale feature detector, which consists of a filter bank and a maximum a posteriori (MAP) classifier. The framework assumes the availability of a one-scale filter with a particular indicator response to the desired feature. This filter is used to generate a multiscale set of discrete filters by sampling on a rectangular lattice to preserve the indicator responses at all the scales. The net step in the framework consists of designing the filter bank to approximate the generated filters. A 2D MAP detector is then designed to minimize detection errors. With the assumption of known feature, the resulting detector depends only on the filter bank, and not on the noise. Relaxing this assumption yields a detection algorithm that is noise dependent and computationally intensive. The framework is applied to edge detection in a noisy environment, and the results indicate efficient detection. Moreover the 2D MAP can find feature end-points by direct processing of the image. This is unlike conventional methods where edges need to be first detected and then processed to locate the corners. Examples are presented to demonstrate the algorithm. ©2005 Copyright SPIE - The International Society for Optical Engineering.
Persistent Identifierhttp://hdl.handle.net/10722/178330
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorHajj, HMen_US
dc.contributor.authorNguyen, TQen_US
dc.contributor.authorChin, RTen_US
dc.date.accessioned2012-12-19T09:46:17Z-
dc.date.available2012-12-19T09:46:17Z-
dc.date.issued1996en_US
dc.identifier.citationProceedings Of Spie - The International Society For Optical Engineering, 1996, v. 2825, p. 330-341en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/178330-
dc.description.abstractThis paper presents a novel methodology for designing a 2D multiscale feature detector, which consists of a filter bank and a maximum a posteriori (MAP) classifier. The framework assumes the availability of a one-scale filter with a particular indicator response to the desired feature. This filter is used to generate a multiscale set of discrete filters by sampling on a rectangular lattice to preserve the indicator responses at all the scales. The net step in the framework consists of designing the filter bank to approximate the generated filters. A 2D MAP detector is then designed to minimize detection errors. With the assumption of known feature, the resulting detector depends only on the filter bank, and not on the noise. Relaxing this assumption yields a detection algorithm that is noise dependent and computationally intensive. The framework is applied to edge detection in a noisy environment, and the results indicate efficient detection. Moreover the 2D MAP can find feature end-points by direct processing of the image. This is unlike conventional methods where edges need to be first detected and then processed to locate the corners. Examples are presented to demonstrate the algorithm. ©2005 Copyright SPIE - The International Society for Optical Engineering.en_US
dc.languageengen_US
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_US
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.subject2D Feature Detectionen_US
dc.subjectBayes Classifieren_US
dc.subjectEdge Detectionen_US
dc.subjectFilter Banken_US
dc.subjectMultirateen_US
dc.subjectMultiscaleen_US
dc.subjectScale-Space Analysisen_US
dc.titleA 2D multirate Bayesian framework for multiscale feature detectionen_US
dc.typeConference_Paperen_US
dc.identifier.emailChin, RT: rchin@hku.hken_US
dc.identifier.authorityChin, RT=rp01300en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1117/12.255244en_US
dc.identifier.scopuseid_2-s2.0-78751630460en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78751630460&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume2825en_US
dc.identifier.spage330en_US
dc.identifier.epage341en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridHajj, HM=35117824200en_US
dc.identifier.scopusauthoridNguyen, TQ=35556344800en_US
dc.identifier.scopusauthoridChin, RT=7102445426en_US

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