Segmentation of color images by chromaticity features using self-organizing maps
Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors....
- Autores:
-
García-Lamont, Farid
Cuevas Rasgado, Alma Delia
Niño Membrillo, Yedid Erandini
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/67618
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/67618
http://bdigital.unal.edu.co/68647/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Segmentation of color images
color spaces
competitive neural networks
Segmentación de imágenes de color
espacios de color
redes neuronales competitivas
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Universidad Nacional de Colombia |
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|
dc.title.spa.fl_str_mv |
Segmentation of color images by chromaticity features using self-organizing maps |
title |
Segmentation of color images by chromaticity features using self-organizing maps |
spellingShingle |
Segmentation of color images by chromaticity features using self-organizing maps 62 Ingeniería y operaciones afines / Engineering Segmentation of color images color spaces competitive neural networks Segmentación de imágenes de color espacios de color redes neuronales competitivas |
title_short |
Segmentation of color images by chromaticity features using self-organizing maps |
title_full |
Segmentation of color images by chromaticity features using self-organizing maps |
title_fullStr |
Segmentation of color images by chromaticity features using self-organizing maps |
title_full_unstemmed |
Segmentation of color images by chromaticity features using self-organizing maps |
title_sort |
Segmentation of color images by chromaticity features using self-organizing maps |
dc.creator.fl_str_mv |
García-Lamont, Farid Cuevas Rasgado, Alma Delia Niño Membrillo, Yedid Erandini |
dc.contributor.author.spa.fl_str_mv |
García-Lamont, Farid Cuevas Rasgado, Alma Delia Niño Membrillo, Yedid Erandini |
dc.subject.ddc.spa.fl_str_mv |
62 Ingeniería y operaciones afines / Engineering |
topic |
62 Ingeniería y operaciones afines / Engineering Segmentation of color images color spaces competitive neural networks Segmentación de imágenes de color espacios de color redes neuronales competitivas |
dc.subject.proposal.spa.fl_str_mv |
Segmentation of color images color spaces competitive neural networks Segmentación de imágenes de color espacios de color redes neuronales competitivas |
description |
Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features. |
publishDate |
2016 |
dc.date.issued.spa.fl_str_mv |
2016-05-01 |
dc.date.accessioned.spa.fl_str_mv |
2019-07-03T04:41:26Z |
dc.date.available.spa.fl_str_mv |
2019-07-03T04:41:26Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
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.coarversion.spa.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
publishedVersion |
dc.identifier.issn.spa.fl_str_mv |
ISSN: 2248-8723 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/67618 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/68647/ |
identifier_str_mv |
ISSN: 2248-8723 |
url |
https://repositorio.unal.edu.co/handle/unal/67618 http://bdigital.unal.edu.co/68647/ |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.spa.fl_str_mv |
https://revistas.unal.edu.co/index.php/ingeinv/article/view/55746 |
dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación Ingeniería e Investigación |
dc.relation.references.spa.fl_str_mv |
García-Lamont, Farid and Cuevas Rasgado, Alma Delia and Niño Membrillo, Yedid Erandini (2016) Segmentation of color images by chromaticity features using self-organizing maps. Ingeniería e Investigación, 36 (2). pp. 78-89. ISSN 2248-8723 |
dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ingeniería |
institution |
Universidad Nacional de Colombia |
bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/67618/1/55746-303227-1-PB.pdf https://repositorio.unal.edu.co/bitstream/unal/67618/2/55746-303227-1-PB.pdf.jpg |
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1814090039924097024 |
spelling |
Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2García-Lamont, Farid506040c4-51ac-4a3d-84a1-6e27f1e5bac1300Cuevas Rasgado, Alma Delia04307d02-5807-4f73-b9d4-2853b86533c2300Niño Membrillo, Yedid Erandini6a351925-ab28-404f-a7fd-e7f5745eadb63002019-07-03T04:41:26Z2019-07-03T04:41:26Z2016-05-01ISSN: 2248-8723https://repositorio.unal.edu.co/handle/unal/67618http://bdigital.unal.edu.co/68647/Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Usualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ingenieríahttps://revistas.unal.edu.co/index.php/ingeinv/article/view/55746Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e InvestigaciónIngeniería e InvestigaciónGarcía-Lamont, Farid and Cuevas Rasgado, Alma Delia and Niño Membrillo, Yedid Erandini (2016) Segmentation of color images by chromaticity features using self-organizing maps. Ingeniería e Investigación, 36 (2). pp. 78-89. ISSN 2248-872362 Ingeniería y operaciones afines / EngineeringSegmentation of color imagescolor spacescompetitive neural networksSegmentación de imágenes de colorespacios de colorredes neuronales competitivasSegmentation of color images by chromaticity features using self-organizing mapsArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL55746-303227-1-PB.pdfapplication/pdf1337477https://repositorio.unal.edu.co/bitstream/unal/67618/1/55746-303227-1-PB.pdf83fbda3b661b46011fd295a6ce82ef8aMD51THUMBNAIL55746-303227-1-PB.pdf.jpg55746-303227-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg8488https://repositorio.unal.edu.co/bitstream/unal/67618/2/55746-303227-1-PB.pdf.jpg7c1938ab3d5c458a17b25d3691180514MD52unal/67618oai:repositorio.unal.edu.co:unal/676182023-05-30 23:03:22.212Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |