Three-dimensional object recognition using x-ray imaging

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dc.contributor.authorYeom, Sko
dc.contributor.authorJavidi, Bko
dc.contributor.authorRoh, YJko
dc.contributor.authorCho, Hyungsuckko
dc.date.accessioned2007-08-06T02:08:51Z-
dc.date.available2007-08-06T02:08:51Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2005-02-
dc.identifier.citationOPTICAL ENGINEERING, v.44, no.2, pp.27201 - 27224-
dc.identifier.issn0091-3286-
dc.identifier.urihttp://hdl.handle.net/10203/960-
dc.description.abstractThis paper presents a distortion-tolerant 3-D volume object recognition technique. Volumetric information on 3-D objects is reconstructed by x-ray imaging. We introduce 3-D feature extraction, volume matching, and statistical significance testing for the 3-D object recognition. The 3D Gabor-based wavelets extract salient features from 3-D volume objects and represent them in the 3-D spatial-frequency domain. Gabor coefficients constitute feature vectors that are invariant to translation, rotation, and distortion. Distortion-tolerant volume matching is performed by a modified 3-D dynamic link association (DLA). The DLA is composed of two stages: rigid motion of a 3-D graph, and elastic deformation of the graph. Our 3-D DLA presents a simple and straightforward solution for a 3-D volume matching task. Finally, significance testing decides the class of input objects in a statistical manner. Experiment and simulation results are presented for five classes of volume objects. We test three classes of synthetic data (pyramid, hemisphere, and cone) and two classes of experimental data (short screw and long screw). The recognition performance is analyzed in terms of the mean absolute error between references and input volume objects. We also confirm the robustness of the recognition algorithm by varying system parameters. (c) 2005 Society of Photo-Optical Instrumentation Engineers.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherSpie-Soc Photo-Optical Instrumentation Engineers-
dc.subjectDIGITAL HOLOGRAPHY-
dc.subjectFEATURE-EXTRACTION-
dc.subjectCLASSIFICATION-
dc.subjectREPRESENTATION-
dc.subjectREGISTRATION-
dc.subjectCORRELATOR-
dc.subjectFREQUENCY-
dc.subjectEFFICIENT-
dc.subjectROBUST-
dc.titleThree-dimensional object recognition using x-ray imaging-
dc.typeArticle-
dc.identifier.wosid000227821100042-
dc.identifier.scopusid2-s2.0-19044395571-
dc.type.rimsART-
dc.citation.volume44-
dc.citation.issue2-
dc.citation.beginningpage27201-
dc.citation.endingpage27224-
dc.citation.publicationnameOPTICAL ENGINEERING-
dc.identifier.doi10.1117/1.1844532-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorCho, Hyungsuck-
dc.contributor.nonIdAuthorYeom, S-
dc.contributor.nonIdAuthorJavidi, B-
dc.contributor.nonIdAuthorRoh, YJ-
dc.type.journalArticleArticle-
dc.subject.keywordAuthor3-D image processing-
dc.subject.keywordAuthorimage reconstruction-
dc.subject.keywordAuthorimage recognition-
dc.subject.keywordAuthorimage classification-
dc.subject.keywordAuthorx-ray imaging systems-
dc.subject.keywordPlusDIGITAL HOLOGRAPHY-
dc.subject.keywordPlusFEATURE-EXTRACTION-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusREPRESENTATION-
dc.subject.keywordPlusREGISTRATION-
dc.subject.keywordPlusCORRELATOR-
dc.subject.keywordPlusFREQUENCY-
dc.subject.keywordPlusEFFICIENT-
dc.subject.keywordPlusROBUST-
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