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		<citationkey>SousaeSantosPedr:2016:ImHuSk</citationkey>
		<title>Improvements on human skin segmentation in digital images</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
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		<author>Sousa e Santos, Anderson Carlos,</author>
		<author>Pedrini, Hélio,</author>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>mr.acarlos@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>segmentation, saliency, texture, skin detection.</keywords>
		<abstract>Human skin segmentation has several applications in computer vision and pattern recognition fields, whose main purpose is to distinguish skin and non-skin regions. Despite the large number of available methods, accurate skin segmentation is still a challenging task. Three main contributions toward this need are presented in this work. The first is a self-contained method for adaptive skin segmentation that adjusts the color model to a particular image. The second is the combination of saliency detection with color skin segmentation, which performs a background removal to eliminate non-skin regions. The third is a texture-based improvement imployed to characterize non-skin regions and thus eliminates color ambiguity adding a second vote. Experimental results on public data sets demonstrate a significant improvement of the proposed methods for human skin segmentation over state-of-the-art approaches.</abstract>
		<language>en</language>
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		<usergroup>mr.acarlos@gmail.com</usergroup>
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