Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance
In this paper a robust algorithm against illumination changes for skin detection in images is proposed. A database with 50 controlled condition images and 50 without controlled conditions of people in frontal position showing face, hands and arms was used. Five algorithms to perform color correction...
- Autores:
-
Arévalo Casallas, Diego Armando
Castañeda Obando, David Ricardo
Castañeda Fandiño, Jos´é Ignacio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/3952
- Acceso en línea:
- http://revistas.uan.edu.co/index.php/ingeuan/article/view/389
http://repositorio.uan.edu.co/handle/123456789/3952
- Palabra clave:
- Faded photo correction
gray world assumption
gamma correction
illumination
skin color
segmentation
euclidean distance
mahalanobis distance
histogram
Corrección foto descolorida
Suposición de mundo gris
Corrección gamma
iluminación
segmentación color de piel
Distancia Euclidiana
Distancia Mahalanobis
Histograma
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arévalo Casallas, Diego ArmandoCastañeda Obando, David RicardoCastañeda Fandiño, Jos´é Ignacio2021-06-16T13:53:12Z2021-06-16T13:53:12Z2014-12-20http://revistas.uan.edu.co/index.php/ingeuan/article/view/389http://repositorio.uan.edu.co/handle/123456789/3952In this paper a robust algorithm against illumination changes for skin detection in images is proposed. A database with 50 controlled condition images and 50 without controlled conditions of people in frontal position showing face, hands and arms was used. Five algorithms to perform color correction are evaluated: Simple Correction with Green Channel, Color Channel Compression, Color Channel Expansion, Fixed Reference and Gamma Correction. And four algorithms for segmentation are evaluated as well: RGB Skin Color, Reference Histogram, Euclidean Distance and Mahalanobis Distance. The proposed algorithm uses the Fixed Reference method together with Gamma Correction for color correction and performs the skin segmentation based on an RCrR color plane, found by making the transformation of the images using RGB and YCbCr spaces, finally Mahalanobis Distance is used. An average sensitivity value of 99.36 % and specificity of 84.31 % were obtained as result.En este artículo se propone un algoritmo robusto ante los cambios de iluminación para la detección de la piel en imágenes, se utiliza una base de datos que consta de 50 imágenes en condiciones controladas y 50 en condiciones no controladas, las imágenes cuentan con personas en forma frontal, mostrando rostro, manos, y brazos. Se evalúan 5 algoritmos para realizar corrección de color los cuales son: Corrección sencilla con canal verde, Compresión canal de color, Expansión canal de color, Referencia fija, Corrección Gamma. Se evalúan 4 algoritmos para segmentación los cuales son: Color de piel en RGB, Referencia de Histograma, Distancia Euclidiana y Distancia de Mahalanobis. El algoritmo propuesto utiliza el método referencia fija unido al algoritmo de corrección gamma para corrección de color y realiza segmentación de la piel a partir de un plano de color RCrR, encontrado de la transformación de las imágenes utilizando los espacios RGB y YCbCr, finalmente utiliza la distancia de Mahalanobis. Como resultado se obtiene un valor promedio de sensibilidad igual 99.36% y de especificidad igual 84.31%.application/pdfspaUniversidad Antonio Nariñohttp://revistas.uan.edu.co/index.php/ingeuan/article/view/389/3282346-14462145-0935INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 5 Núm. 9 (2014)Faded photo correctiongray world assumptiongamma correctionilluminationskin colorsegmentationeuclidean distancemahalanobis distancehistogramCorrección foto descoloridaSuposición de mundo grisCorrección gammailuminaciónsegmentación color de pielDistancia EuclidianaDistancia MahalanobisHistogramaRobust Visual Segmentation using RCrR Plane and Mahalanobis DistanceSegmentación Visual Robusta utilizando el Plano RCrR y la Distancia de Mahalanobisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85123456789/3952oai:repositorio.uan.edu.co:123456789/39522024-10-09 23:03:10.515https://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertometadata.onlyhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co |
| dc.title.en-US.fl_str_mv |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| dc.title.es-ES.fl_str_mv |
Segmentación Visual Robusta utilizando el Plano RCrR y la Distancia de Mahalanobis |
| title |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| spellingShingle |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance Faded photo correction gray world assumption gamma correction illumination skin color segmentation euclidean distance mahalanobis distance histogram Corrección foto descolorida Suposición de mundo gris Corrección gamma iluminación segmentación color de piel Distancia Euclidiana Distancia Mahalanobis Histograma |
| title_short |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| title_full |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| title_fullStr |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| title_full_unstemmed |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| title_sort |
Robust Visual Segmentation using RCrR Plane and Mahalanobis Distance |
| dc.creator.fl_str_mv |
Arévalo Casallas, Diego Armando Castañeda Obando, David Ricardo Castañeda Fandiño, Jos´é Ignacio |
| dc.contributor.author.spa.fl_str_mv |
Arévalo Casallas, Diego Armando Castañeda Obando, David Ricardo Castañeda Fandiño, Jos´é Ignacio |
| dc.subject.en-US.fl_str_mv |
Faded photo correction gray world assumption gamma correction illumination skin color segmentation euclidean distance mahalanobis distance histogram |
| topic |
Faded photo correction gray world assumption gamma correction illumination skin color segmentation euclidean distance mahalanobis distance histogram Corrección foto descolorida Suposición de mundo gris Corrección gamma iluminación segmentación color de piel Distancia Euclidiana Distancia Mahalanobis Histograma |
| dc.subject.es-ES.fl_str_mv |
Corrección foto descolorida Suposición de mundo gris Corrección gamma iluminación segmentación color de piel Distancia Euclidiana Distancia Mahalanobis Histograma |
| description |
In this paper a robust algorithm against illumination changes for skin detection in images is proposed. A database with 50 controlled condition images and 50 without controlled conditions of people in frontal position showing face, hands and arms was used. Five algorithms to perform color correction are evaluated: Simple Correction with Green Channel, Color Channel Compression, Color Channel Expansion, Fixed Reference and Gamma Correction. And four algorithms for segmentation are evaluated as well: RGB Skin Color, Reference Histogram, Euclidean Distance and Mahalanobis Distance. The proposed algorithm uses the Fixed Reference method together with Gamma Correction for color correction and performs the skin segmentation based on an RCrR color plane, found by making the transformation of the images using RGB and YCbCr spaces, finally Mahalanobis Distance is used. An average sensitivity value of 99.36 % and specificity of 84.31 % were obtained as result. |
| publishDate |
2014 |
| dc.date.issued.spa.fl_str_mv |
2014-12-20 |
| dc.date.accessioned.none.fl_str_mv |
2021-06-16T13:53:12Z |
| dc.date.available.none.fl_str_mv |
2021-06-16T13:53:12Z |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/389 |
| dc.identifier.uri.none.fl_str_mv |
http://repositorio.uan.edu.co/handle/123456789/3952 |
| url |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/389 http://repositorio.uan.edu.co/handle/123456789/3952 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/389/328 |
| dc.rights.none.fl_str_mv |
Acceso abierto |
| dc.rights.license.spa.fl_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Acceso abierto https://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Antonio Nariño |
| dc.source.none.fl_str_mv |
2346-1446 2145-0935 |
| dc.source.es-ES.fl_str_mv |
INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 5 Núm. 9 (2014) |
| institution |
Universidad Antonio Nariño |
| repository.name.fl_str_mv |
Repositorio Institucional UAN |
| repository.mail.fl_str_mv |
alertas.repositorio@uan.edu.co |
| _version_ |
1814300383579734016 |