Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyConsularoCesa:2005:QuInGr
TitleQuadtree-based inexact graph matching for image analysis
Access Date2022, Jan. 22
Number of Files1
Size208 KiB
Context area
Author1 Consularo, Luís Augusto
2 Cesar Jr, Roberto Marcondes
Affiliation1 UNIMEP - Methodist University of Piracicaba
2 IME-USP - Department of Computer Science - IME - University of São Paulo
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2005-07-15 21:42:07 :: consularo -> banon ::
2005-07-18 14:24:29 :: banon -> consularo ::
2008-07-17 14:11:01 :: consularo -> banon ::
2008-08-26 15:17:03 :: banon -> administrator ::
2009-08-13 20:37:54 :: administrator -> banon ::
2010-08-28 20:01:19 :: banon -> administrator ::
2020-02-19 03:19:19 :: administrator -> :: 2005
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsinexact graph matching
AbstractThis paper presents a new method for segmentation and recognition of image objects based on structural pattern recognition. The input image is decomposed into regions through a quadtree algorithm. The decomposed image is represented by an attributed relational graph (ARG) named input graph. The objects to be recognized are also stored in an ARG named model graph. Object segmentation and recognition are accomplished by matching the input graph to the model graph. The possible inexact matches between the two graphs are cliques of the association graph between them. An objective function, to be optimized, is defined for each clique in order to measure how suitable is the match between the graphs. Therefore, recognition is modeled as an optimization procedure. A beam-search algorithm is used to optimize the objective function. Experimental results corroborating the proposed approach are presented.
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