Identity statement area
Reference TypeConference Proceedings
Last Update2006: administrator
Metadata Last Update2020: administrator
Citation KeyCuadros-VargasGerCasBatNon:2006:Im2DMe
TitleImproving 2D mesh image segmentation with Markovian Random Fields
Date8-11 Oct. 2006
Access Date2020, Dec. 05
Number of Files1
Size2118 KiB
Context area
Author1 Cuadros-Vargas, Alex J.
2 Gerhardinger, Leandro C.
3 Castro, Mário
4 Batista Neto, João
5 Nonato, Luis G.
Affiliation1 ICMC - Instituto de Ciências Matemáticas e de Computação - USP
2 ICMC - Instituto de Ciências Matemáticas e de Computação - USP
3 ICMC - Instituto de Ciências Matemáticas e de Computaão - USP
4 ICMC - Instituto de Ciências Matemáticas e de Computaão - USP
5 ICMC - Instituto de Ciências Matemáticas e de Computaão - USP
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2006-08-24 16:48:49 :: jdesbn -> banon ::
2006-08-30 21:49:34 :: banon -> jdesbn ::
2008-07-17 14:11:04 :: jdesbn -> administrator ::
2009-08-13 20:38:15 :: administrator -> banon ::
2010-08-28 20:02:25 :: banon -> administrator ::
2020-02-19 03:17:49 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsPolygonal Meshes, Texture Analysis, Segmentation.
AbstractTraditional mesh segmentation methods normally operate on geometrical models with no image information. On the other hand, 2D image-based mesh generation and segmentation counterparts, such as Imesh cite{Vargas:05} perform the task by following a set of well defined rules derived from the geometry of the triangles, but with no statistical information of the mesh elements. This paper presents a novel segmentation method that combines the original Imesh image-based segmentation approach with Markovian Random Field (MRF) models. It takes an image as input, generate a mesh of triangles and, by treating the mesh as a Markovian field, produces quality unsupervised segmentation. The results have demonstrated that the method not only provides better segmentation than that of original Imesh, but is also capable of producing MRF-like segmentation output for certain types of images, with considerable cut in processing times.
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Target FileVargas-MRF_Mesh.pdf
User Groupjdesbn
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