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dc.contributor.authorWang, Tsaipeien_US
dc.date.accessioned2014-12-08T15:04:08Z-
dc.date.available2014-12-08T15:04:08Z-
dc.date.issued2008en_US
dc.identifier.isbn978-1-4244-1818-3en_US
dc.identifier.issn1098-7584en_US
dc.identifier.urihttp://hdl.handle.net/11536/2641-
dc.description.abstractWe present in this paper a new type of alternating-optimization based possibilistic c-shell algorithm for clustering template-based shapes. A cluster prototype consists of a copy of the template after translation, seating, rotation, and/or affine transformations. We use a number of two-dimensional data sets, both synthetic and from real-world images, to illustrate the capability of our algorithm in detecting generic template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements of known number of dusters and good initialization.en_US
dc.language.isoen_USen_US
dc.titlePossibilistic Clustering of Generic Shapes Derived from Templatesen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5en_US
dc.citation.spage1723en_US
dc.citation.epage1730en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000262974001037-
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