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dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorCaelli, Terryen_US
dc.contributor.editorIAPRen_US
dc.date.accessioned2017-04-24T12:26:34Z
dc.date.available2017-04-24T12:26:34Z
dc.date.issued2010en_US
dc.date.modified2011-04-18T06:57:11Z
dc.identifier.refurihttp://www.icpr2010.org/en_AU
dc.identifier.doi10.1109/ICPR.2010.550en_AU
dc.identifier.urihttp://hdl.handle.net/10072/36156
dc.description.abstractMicropattern based image representation and recognition, e.g. Local Binary Pattern (LBP), has been proved successful over the past few years due to its advantages of illumination tolerance and computational efficiency. However, LBP only encodes the first-order radial-directional derivatives of spatial images and is inadequate to completely describe the discriminative features for classification. This paper proposes a new Circular Derivative Pattern (CDP) which extracts high-order derivative information of images along circular directions. We argue that the high-order circular derivatives contain more detailed and more discriminative information than the first-order LBP in terms of recognition accuracy. Experimental evaluation through face recognition on the FERET database and insect classification on the NICTA Biosecurity Dataset demonstrated the effectiveness of the proposed method.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent617121 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameThe 20th International Conference on Pattern Recognition (ICPR 2010)en_US
dc.relation.ispartofconferencetitleProceedings of the 20th International Conference on Pattern Recognition (ICPR 2010)en_US
dc.relation.ispartofdatefrom2010-08-23en_US
dc.relation.ispartofdateto2010-08-26en_US
dc.relation.ispartoflocationIstanbul, Turkeyen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchComputer Visionen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080104en_US
dc.subject.fieldofresearchcode080109en_US
dc.titleHigh-Order Circular Derivative Pattern for Image Representation and Recognitionen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2010 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_AU
gro.date.issued2010
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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