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dc.contributor.authorWilliams, David
dc.contributor.authorGroen, Johannes
dc.date.accessioned2018-10-11T14:09:39Z
dc.date.available2018-10-11T14:09:39Z
dc.date.issued2009/12
dc.identifier36661
dc.identifier.govdocNURC-PR-2009-006
dc.identifier.urihttp://hdl.handle.net/20.500.12489/652
dc.description.abstractThis work proposes an elegantly simple solution to the general task of classifying the shape of an object that has been viewed multiple times. Specifically, this problem is addressed in the context of underwater mine classification where the objectiveis to discriminate targets (i.e., mines) from benign clutter (e.g., rocks) when each object is observed in an arbitrary number of synthetic aperture sonar (SAS) images. The proposed multi-view classification algorithm is based on finding the single highest maximum correlation between (i) a set of views of a training shape of interest and (ii) a set of views of a given testing object. Classification is performed by using this measure of similarity, which we term the affinity, directly. This approach obviates the need for explicit feature extraction and classifier construction. Moreover, the framework induces no constraints on the number of views that each object can possess. Promising experimental results usingreal SAS imagery demonstrate the feasibility of the proposed approach for multi-view classification of underwater mines. In particular, it is shown that classification performance improves dramatically as the number of views of the objects increases.
dc.format9 p. : ill. (digital, PDF file)
dc.languageEnglish
dc.publisherNURC
dc.sourceOriginally published in: Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece.
dc.subjectSynthetic Aperture Sonar (SAS)
dc.subjectNaval mines
dc.subjectMine countermeasures (MCM)
dc.subjectSonar images
dc.subjectImage processing
dc.subjectTarget classification
dc.subjectAlgorithms
dc.titleMulti-view target classification in synthetic aperture sonar imagery
dc.typeReprint (PR)
dc.typePapers and Articles


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