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 |