Skin detection in digital images with artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/185311 |
Resumo: | The increasing capacity of data processing in personal computers and devices could develop filters and automatic classifiers working in real time and applied in several areas. Considering Digital Image Processing and Artificial Neural Networks, these filters emulate the human perception searching for patterns in order to identify specific features. Filters which the main goal is to restrict the access to inappropriate content starts identifying skin tones - the main evidence of human presence in a picture. Although being complex and robust, if the classifier is not able to identify distinct skin tones under random capture conditions, the accuracy is minimal. Facing several ways on describing skin tones over different color spaces, this work uses the RGB, YCbCr and HSV color spaces which are widely applied in recording devices (photographic and digital cameras for example). Based on the examples shown during the training phase, the ANNs must be able to classify skin tones into two distinct groups: skin and non skin. |
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Repositório Institucional da UNESP |
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Skin detection in digital images with artificial neural networksartificial neural networkdigital image processingpattern recognitionThe increasing capacity of data processing in personal computers and devices could develop filters and automatic classifiers working in real time and applied in several areas. Considering Digital Image Processing and Artificial Neural Networks, these filters emulate the human perception searching for patterns in order to identify specific features. Filters which the main goal is to restrict the access to inappropriate content starts identifying skin tones - the main evidence of human presence in a picture. Although being complex and robust, if the classifier is not able to identify distinct skin tones under random capture conditions, the accuracy is minimal. Facing several ways on describing skin tones over different color spaces, this work uses the RGB, YCbCr and HSV color spaces which are widely applied in recording devices (photographic and digital cameras for example). Based on the examples shown during the training phase, the ANNs must be able to classify skin tones into two distinct groups: skin and non skin.UNESP, Elect Engn Dept, Campus Ilha Solteira, Ilha Solteira, SP, BrazilUNESP, Elect Engn Dept, Campus Ilha Solteira, Ilha Solteira, SP, BrazilIeeeUniversidade Estadual Paulista (Unesp)Vicentini, Rafael Estefano [UNESP]Lotufo, Anna Diva P. [UNESP]IEEE2019-10-04T12:34:24Z2019-10-04T12:34:24Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62018 Argentine Conference On Automatic Control (aadeca). New York: Ieee, 6 p., 2018.http://hdl.handle.net/11449/185311WOS:000455662900064Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPpor2018 Argentine Conference On Automatic Control (aadeca)info:eu-repo/semantics/openAccess2024-07-04T19:11:33Zoai:repositorio.unesp.br:11449/185311Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:00:34.350805Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Skin detection in digital images with artificial neural networks |
title |
Skin detection in digital images with artificial neural networks |
spellingShingle |
Skin detection in digital images with artificial neural networks Vicentini, Rafael Estefano [UNESP] artificial neural network digital image processing pattern recognition |
title_short |
Skin detection in digital images with artificial neural networks |
title_full |
Skin detection in digital images with artificial neural networks |
title_fullStr |
Skin detection in digital images with artificial neural networks |
title_full_unstemmed |
Skin detection in digital images with artificial neural networks |
title_sort |
Skin detection in digital images with artificial neural networks |
author |
Vicentini, Rafael Estefano [UNESP] |
author_facet |
Vicentini, Rafael Estefano [UNESP] Lotufo, Anna Diva P. [UNESP] IEEE |
author_role |
author |
author2 |
Lotufo, Anna Diva P. [UNESP] IEEE |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Vicentini, Rafael Estefano [UNESP] Lotufo, Anna Diva P. [UNESP] IEEE |
dc.subject.por.fl_str_mv |
artificial neural network digital image processing pattern recognition |
topic |
artificial neural network digital image processing pattern recognition |
description |
The increasing capacity of data processing in personal computers and devices could develop filters and automatic classifiers working in real time and applied in several areas. Considering Digital Image Processing and Artificial Neural Networks, these filters emulate the human perception searching for patterns in order to identify specific features. Filters which the main goal is to restrict the access to inappropriate content starts identifying skin tones - the main evidence of human presence in a picture. Although being complex and robust, if the classifier is not able to identify distinct skin tones under random capture conditions, the accuracy is minimal. Facing several ways on describing skin tones over different color spaces, this work uses the RGB, YCbCr and HSV color spaces which are widely applied in recording devices (photographic and digital cameras for example). Based on the examples shown during the training phase, the ANNs must be able to classify skin tones into two distinct groups: skin and non skin. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2019-10-04T12:34:24Z 2019-10-04T12:34:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2018 Argentine Conference On Automatic Control (aadeca). New York: Ieee, 6 p., 2018. http://hdl.handle.net/11449/185311 WOS:000455662900064 |
identifier_str_mv |
2018 Argentine Conference On Automatic Control (aadeca). New York: Ieee, 6 p., 2018. WOS:000455662900064 |
url |
http://hdl.handle.net/11449/185311 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
2018 Argentine Conference On Automatic Control (aadeca) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128738690334720 |