Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeyRittnerFlorLotu:2007:TeMoGr
TitleNew tensorial representation of color images: tensorial morphological gradient applied to color image segmentation
FormatPrinted, On-line.
DateOct. 7-10, 2007
Access Date2021, Jan. 16
Number of Files1
Size829 KiB
Context area
Author1 Rittner, Leticia
2 Flores, Franklin
3 Lotufo, Roberto
Affiliation1 Faculdade de Engenharia Elétrica e de Computação - UNICAMP
2 Faculdade de Engenharia Elétrica e de Computação - UNICAMP
3 Faculdade de Engenharia Elétrica e de Computação - UNICAMP
EditorFalcão, Alexandre Xavier
Lopes, Hélio Côrtes Vieira
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2007-07-23 18:02:49 :: -> administrator ::
2007-08-02 21:17:33 :: administrator -> ::
2008-07-17 14:09:43 :: -> administrator ::
2009-08-13 20:38:25 :: administrator -> banon ::
2010-08-28 20:02:27 :: banon -> administrator ::
2020-02-19 03:06:19 :: administrator -> :: 2007
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordscolor image segmentation, color gradient, watershed transform.
AbstractThis paper proposes a new Tensorial Representation of HSI color images, where each pixel is a 2 X 2 second order tensor, that can be represented by an ellipse. A proposed tensorial morphological gradient (TMG) is defined as the maximum dissimilarity over the neighborhood determined by a structuring element, and is used in the watershed segmentation framework. Many tensor dissimilarity functions are tested and other color gradients are compared. The comparison uses a new methodology for qualitative evaluation of color image segmentation by watershed, where the watershed lines of the n most significant regions are overlaid on the original image for visual comparison. Experiments show that the TMG using Frobenius norm dissimilarity function presents superior segmentation results, in comparison to other tested gradients.
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