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dc.contributor.author Cervantes Canales, Jair
dc.contributor.author García Lamont, Farid
dc.contributor.author LOPEZ CHAU, ASDRUBAL
dc.creator Cervantes Canales, Jair; 101829
dc.creator García Lamont, Farid; 216477
dc.creator LOPEZ CHAU, ASDRUBAL; 100664
dc.date.accessioned 2018-09-21T22:27:29Z
dc.date.available 2018-09-21T22:27:29Z
dc.date.issued 2018-08-01
dc.identifier.issn 0941-0643
dc.identifier.uri http://hdl.handle.net/20.500.11799/94751
dc.description En este trabajo se presenta una propuesta para segmentación de imágenes por características de color utilizando mapas auto organizados. es
dc.description.abstract Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive. es
dc.language.iso eng es
dc.publisher Neural Computing and Applications es
dc.rights openAccess
dc.rights.uri http://creativecommons.org/licenses/by/4.0
dc.subject Self-organizing maps es
dc.subject Color classification es
dc.subject Image segmentation es
dc.subject Color spaces es
dc.subject.classification INGENIERÍA Y TECNOLOGÍA
dc.title Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity es
dc.type Artículo
dc.provenance Científica
dc.road Dorada
dc.organismo Centro Universitario UAEM Texcoco es
dc.ambito Internacional es
dc.cve.progEstudios 663 es
dc.audience students
dc.audience researchers
dc.type.conacyt article
dc.identificator 7


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  • Título
  • Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity
  • Autor
  • Cervantes Canales, Jair
  • García Lamont, Farid
  • LOPEZ CHAU, ASDRUBAL
  • Fecha de publicación
  • 2018-08-01
  • Editor
  • Neural Computing and Applications
  • Tipo de documento
  • Artículo
  • Palabras clave
  • Self-organizing maps
  • Color classification
  • Image segmentation
  • Color spaces
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

Mostrar el registro sencillo del objeto digital

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