Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2006: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyAudigierLotu:2006:DuThWa
TitleDuality between the watershed by image foresting transform and the fuzzy connectedness segmentation approaches
Access Date2022, Jan. 21
Number of Files1
Size154 KiB
Context area
Author1 Audigier, Romaric
2 Lotufo, Roberto
Affiliation1 FEEC - Unicamp
2 FEEC - Unicamp
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2006-07-17 01:16:40 :: audigier -> banon ::
2006-08-30 21:46:31 :: banon -> audigier ::
2008-07-17 14:11:03 :: audigier -> administrator ::
2009-08-13 20:38:06 :: administrator -> banon ::
2010-08-28 20:02:23 :: banon -> administrator ::
2020-02-19 03:17:38 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsimage segmentation
mathematical morphology
image foresting transform
graph theory
fuzzy connectedness
AbstractThis paper makes a rereading of two successful image segmentation approaches, the fuzzy connectedness (FC) and the watershed (WS) approaches, by analyzing both by means of the Image Foresting Transform (IFT). This graph-based transform provides a sound framework for analyzing and implementing these methods. This paradigm allows to show the duality existing between the WS by IFT and the FC segmentation approaches. Both can be modeled by an optimal forest computation in a dual form (maximization of the similarities or minimization of the dissimilarities), the main difference being the input parameters: the weights associated to each arc of the graph representing the image. In the WS approach, such weights are based on the (possibly filtered) image gradient values whereas they are based on much more complex affinity values in the FC theory. An efficient algorithm for both FC and IFT-WS computation is proposed. Segmentation robustness issue is also discussed.
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