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		<citationkey>LopesCons:2005:RBPeMo</citationkey>
		<title>A RBFN perceptive model for image thresholding</title>
		<format>On-line</format>
		<year>2005</year>
		<date>9-12 Oct. 2005</date>
		<numberoffiles>1</numberoffiles>
		<size>385 KiB</size>
		<author>Lopes, Fabrício Martins,</author>
		<author>Consularo, Luís Augusto,</author>
		<affiliation>CEFET-PR - Centro Federal de Educação Tecnológica do Paraná</affiliation>
		<affiliation>Av. Alberto Carazzai, 1640, 86300-000, Cornélio Procópio, PR, Brasil.,</affiliation>
		<affiliation>UNIMEP - Universidade Metodista de Piracicaba</affiliation>
		<affiliation>Rodovia do Açúcar, Km 156, 13400-911, Piracicaba, SP, Brasil.,</affiliation>
		<editor>Rodrigues, Maria Andréia Formico,</editor>
		<editor>Frery, Alejandro César,</editor>
		<e-mailaddress>fabricio@cp.cefetpr.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>Segmentation, Thresholding, RBFN, Psychophysical.</keywords>
		<abstract>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.</abstract>
		<language>en</language>
		<targetfile>lopesf_rbfnperceptive.pdf</targetfile>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/banon/2005/07.15.21.19</url>
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