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Image filtering and segmentation using kernel density estimation [articol]

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dc.contributor.author Gui, Vasile
dc.contributor.author Laitinen, Jyrki
dc.contributor.author Alexa, Florin
dc.date.accessioned 2020-04-27T06:30:31Z
dc.date.accessioned 2021-03-01T08:39:40Z
dc.date.available 2020-04-27T06:30:31Z
dc.date.available 2021-03-01T08:39:40Z
dc.date.issued 2008
dc.identifier.citation Gui, Vasile. Image filtering and segmentation using kernel density estimation. Timişoara: Editura Politehnica, 2008 en_US
dc.identifier.uri http://primo.upt.ro:1701/primo-explore/search?query=any,contains,Pipeline%20identification%20in%20a%20TDOA%20experiment&tab=default_tab&search_scope=40TUT&vid=40TUT_V1&lang=ro_RO&offset=0 Link Primo
dc.description.abstract Kernel density estimation and mode finding techniques play an active role in solving contemporary computer vision problems, like edge preserving smoothing, segmentation, registration, motion estimation and tracking. The mean shift algorithm is a popular approach to locate density modes. Recently we proposed the multiscale mode filter, a generalization of the mean shift filter, which is able to avoid spurious modes while minimizing outlier sensitivity. In this paper we evaluate the effectiveness of the multiscale mode filter in edge preserving smoothing and image segmentation. en_US
dc.language.iso en en_US
dc.publisher Timişoara:Editura Politehnica en_US
dc.relation.ispartofseries Seria electronică şi telecomunicaţii;Tom 53(67), fasc. 2 (2008), p. 177-182
dc.subject Edge preserving smoothing en_US
dc.subject Multiscale en_US
dc.subject Mode location en_US
dc.subject Mean shift en_US
dc.subject Segmentation en_US
dc.title Image filtering and segmentation using kernel density estimation [articol] en_US
dc.type Article en_US

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