Class separation improvements in pixel classification using colour injection
Autores
Blanco, Edward; Mazo Quintas, Manuel Ramón; Bergasa Pascual, Luis Miguel; Palazuelos Cagigas, Sira Elena; Rodríguez Ascariz, José Manuel; [et al.]Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/43549DOI: 10.3390/s100807803
ISSN: 1424-8220
Editor
MDPI
Fecha de publicación
2010-08-20Patrocinadores
Ministerio de Ciencia e Innovación
Cita bibliográfica
Blanco, E., Mazo, M., Bergasa, L., Palazuelos, S., Rodríguez, J., Losada, C. & Martín, J. 2010, “Class separation improvements in pixel classification using colour injection”, Sensors, vol. 10, no. 8, pp. 7803-7842
Palabras clave
Pixel classification
Colour clustering
Colour segmentation
Class separation
Colour sub-spaces
Colour injection
Proyectos
TIN2009-08984 (Ministerio de Ciencia e Innovación)
TIN2008-06856-C05-05 (Ministerio de Ciencia e Innovación)
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.3390/s100807803Derechos
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
This paper presents an improvement in the colour image segmentation in the Hue Saturation (HS) sub-space. The authors propose to inject (add) a colour vector in the Red Green Blue (RGB) space to increase the class separation in the HS plane. The goal of the work is the development of an algorithm to obtain the optimal colour vector for injection that maximizes the separation between the classes in the HS plane. The chromatic Chrominace-1 Chrominance-2 sub-space (of the Luminance Chrominace-1 Chrominance-2 (YC1C2) space) is used to obtain the optimal vector to add. The proposal is applied on each frame of a colour image sequence in real-time. It has been tested in applications with reduced contrast between the colours of the background and the object, and particularly when the size of the object is very small in comparison with the size of the captured scene. Numerous tests have confirmed that this proposal improves the segmentation process, considerably reducing the effects of the variation of the light intensity of the scene. Several tests have been made in skin segmentation in applications for sign language recognition via computer vision, where an accurate segmentation of hands and face is required.
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