Anomaly Detection Using Classified Eigenblocks in GPR Image

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dc.contributor.authorKim, Min Juko
dc.contributor.authorLee, Seung Euiko
dc.contributor.authorKim, Seong Daeko
dc.date.accessioned2016-07-07T06:32:24Z-
dc.date.available2016-07-07T06:32:24Z-
dc.date.created2016-06-20-
dc.date.created2016-06-20-
dc.date.created2016-06-20-
dc.date.created2016-06-20-
dc.date.issued2016-04-
dc.identifier.citationDefense and Security and commercial imaging-
dc.identifier.urihttp://hdl.handle.net/10203/210094-
dc.description.abstractAutomatic landmine detection system using ground penetrating radar has been widely researched. For the automatic mine detection system, system speed is an important factor. Many techniques for mine detection have been developed based on statistical background. Among them, a detection technique employing the Principal Component Analysis(PCA) has been used for clutter reduction and anomaly detection. However, the PCA technique can retard the entire process, because of large basis dimension and a numerous number of inner product operations. In order to overcome this problem, we propose a fast anomaly detection system using 2D DCT and PCA. Our experiments use a set of data obtained from a test site where the anti-tank and antipersonnel mines are buried. We evaluate the proposed system in terms of the ROC curve. The result shows that the proposed system p-
dc.languageEnglish-
dc.publisherSPIE-
dc.titleAnomaly Detection Using Classified Eigenblocks in GPR Image-
dc.typeConference-
dc.identifier.wosid000383223700044-
dc.identifier.scopusid2-s2.0-84982182439-
dc.type.rimsCONF-
dc.citation.publicationnameDefense and Security and commercial imaging-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationBaltimore Convention Center-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorKim, Seong Dae-
dc.contributor.nonIdAuthorLee, Seung Eui-
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EE-Conference Papers(학술회의논문)
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