Segmentación de Imágenes en Color utilizando Histogramas Bi–Variables en Espacios Color Polares Luminancia/Saturación/Matiz
Image Color Segmentation using Bi–variate Histograms in Luminance/Saturation/Hue Polar Color Spaces1
Jesús Angulo y Jean Serra
Centre de Morphologie Mathématique, Ecole des Mines de Paris
35, rue Saint–Honoré, 77305 Fontainebleau, Francia
e–mail: angulo@cmm.ensmp.fr, serra@cmm.ensmp.fr
Web: http://cmm.ensmp.fr/~angulo
Artículo recibido en mayo 20, 2003; aceptado en marzo 25, 2005
1 A preliminary version in englishof this paper is available from the authors on request: Centre de Morphologie Mathématique–EMP, Internal Note N–O3/03/MM, January 2003.
Resumen
]]> La elección de un espacio de representación adecuado para el color sigue constituyendo un reto en procesado y análisis de las imágenes en color. A partir de una familia de espacios en coordenadas polares de tipo luminancia/saturación/matiz (LSM) recientemente propuesta (mejorando al sistema HLS), y que tienen características apropiadas para el tratamiento cuantitativo, se derivan dos histogramas bi–variables: histr;HS (tratando conjuntamente la componente de matiz y la componente de saturación) y histLS (componentes luminancia y saturación) asociados a estos espacios de color. A continuación, se muestra un método morfológico para el agrupamiento de los puntos en los histogramas bi–variables, fundado en la transformación de la línea divisoria de aguas. Después, se obtienen dos particiones (cromática y acromática) por proyección inversa de los histogramas segmentados sobre el espacio de la imagen color inicial. Una combinación de las dos particiones, basada en la saturación, proporciona un método interesante para la segmentación de imágenes en color.Palabras clave: imágenes en color, espacio color LSM, histográmas bi–variables, morfología matemática, transformación línea divisoria de aguas, segmentación color, clasificación morfológica
Abstract
The choice of a suitable colour space representation is still a challenging task in the processing and analysis of colour images. Starting with the recently proposed family of polar coordinate systems LSH (improving the standard HLS) which have suitable properties for quantitative image processing, the derivation of two bivariate histograms: histr;HS (putting together the Hue component and the Saturation component) and histLS (Luminance and Saturation components) associated to these colour spaces is presented. A method for the morphological clustering of the points in the bivariates histograms is shown, relying on the watershed transformation. Then, by back projecting on the space of the initial colour image, two partitions (chromatic and achromatic) are obtained. A saturation–based combination of the two partitions yields an interesting method for segmenting colour images.
Keywords: colour images, LSH colour space, bi–variant histograms, mathematical morphology, watershed transformation, colour segmentation, morphological clustering.
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