%0 Conference Proceedings
%4 sid.inpe.br/banon/2005/07.15.21.19
%2 sid.inpe.br/banon/2005/07.15.21.19.20
%@doi 10.1109/SIBGRAPI.2005.8
%T A RBFN perceptive model for image thresholding
%D 2005
%A Lopes, Fabrício Martins,
%A Consularo, Luís Augusto,
%@affiliation CEFET-PR - Centro Federal de Educação Tecnológica do Paraná
%@affiliation Av. Alberto Carazzai, 1640, 86300-000, Cornélio Procópio, PR, Brasil.,
%@affiliation UNIMEP - Universidade Metodista de Piracicaba
%@affiliation Rodovia do Açúcar, Km 156, 13400-911, Piracicaba, SP, Brasil.,
%E Rodrigues, Maria Andréia Formico,
%E Frery, Alejandro César,
%B Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
%C Natal, RN, Brazil
%8 9-12 Oct. 2005
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Segmentation, Thresholding, RBFN, Psychophysical.
%X The 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.
%@language en
%3 lopesf_rbfnperceptive.pdf