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		<doi>10.1109/SIBGRAPI.2009.47</doi>
		<citationkey>KuiaskiVieiBorbGamb:2009:StEfIl</citationkey>
		<title>A Study of the Effect of Illumination Conditions and Color Spaces on Skin Segmentation</title>
		<format>Printed, On-line.</format>
		<year>2009</year>
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		<author>Kuiaski, Diogo,</author>
		<author>Vieira Neto, Hugo,</author>
		<author>Borba, Gustavo,</author>
		<author>Gamba, Humberto,</author>
		<affiliation>Post-graduation Program in Electrical Engineering and Industrial Informatics, Federal University of Techology - Paraná</affiliation>
		<affiliation>Post-graduation Program in Electrical Engineering and Industrial Informatics, Federal University of Techology - Paraná</affiliation>
		<affiliation>Post-graduation Program in Electrical Engineering and Industrial Informatics, Federal University of Techology - Paraná</affiliation>
		<affiliation>Post-graduation Program in Electrical Engineering and Industrial Informatics, Federal University of Techology - Paraná</affiliation>
		<editor>Nonato, Luis Gustavo,</editor>
		<editor>Scharcanski, Jacob,</editor>
		<e-mailaddress>hvieir@utfpr.edu.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio de Janeiro, RJ, Brazil</conferencelocation>
		<date>11-14 Oct. 2009</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>skin segmentation, Bayes theory, image processing, color spaces.</keywords>
		<abstract>This work aims at investigating the influence of luminance information and environment illumination on skin classification. We explore Bayesian  approaches to perform automatic classification of human skin pixels on digital images, using color features as input. Two probabilistic skin color models were built on different color spaces (RGB, normalized RG, HSI, HS, YCbCr and CbCr) and tested in a task of automatic pixel classification into skin and non-skin. Analyses of classification performance were done by presenting an illumination controlled image database containing images acquired in four different illumination conditions (shadow, sun, incandescent and fluorescent lights) to these classifiers. Our experiments show that building probabilistic skin color models using the CbCr color space generally improves performance of the classifiers and that best performance is achieved in shadow illumination.</abstract>
		<language>en</language>
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		<usergroup>hvieir@utfpr.edu.br</usergroup>
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