Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyLopesCons:2005:RBPeMo
TitleA RBFN perceptive model for image thresholding
Access Date2021, Nov. 27
Number of Files1
Size385 KiB
Context area
Author1 Lopes, Fabrício Martins
2 Consularo, Luís Augusto
Affiliation1 CEFET-PR - Centro Federal de Educação Tecnológica do Paraná
2 Av. Alberto Carazzai, 1640, 86300-000, Cornélio Procópio, PR, Brasil.
3 UNIMEP - Universidade Metodista de Piracicaba
4 Rodovia do Açúcar, Km 156, 13400-911, Piracicaba, SP, Brasil.
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 21:19:21 :: -> banon ::
2005-07-18 14:25:29 :: banon -> ::
2008-07-17 14:11:01 :: -> banon ::
2008-08-26 15:17:03 :: banon -> administrator ::
2009-08-13 20:37:58 :: administrator -> banon ::
2010-08-28 20:01:20 :: banon -> administrator ::
2020-02-19 03:19:22 :: administrator -> :: 2005
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Content Stagecompleted
Content TypeExternal Contribution
AbstractThe digital image segmentation challenge has demanded the development of a plethora of methods and approaches. A quite simple approach, the thresholding, has still been intensively applied mainly for real-time vision applications. However, the threshold criteria often depend on entropic or statistical image features. This work searches a relationship between these features and subjective human threshold decisions. Then, an image thresholding model based on these subjective decisions and global statistical features was developed by training a Radial Basis Functions Network (RBFN). This work also compares the automatic thresholding methods to the human responses. Furthermore, the RBFN-modeled answers were compared to the automatic thresholding. The results show that entropic-based method was closer to RBFN-modeled thresholding than variance-based method. It was also found that another automatic method which combines global and local criteria presented higher correlation with human responses.
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