Artículos

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: hist^r;HS (tratando conjuntamente la componente de matiz y la componente de saturación) y hist_LS (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: hist^r;HS (putting together the Hue component and the Saturation component) and hist_LS (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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Referencias

1. Albiol A., L. Torres and Delp E.J., "An unsupervised color image segmentation algorithm for face dectection applications", Proceedings of the IEEE International Conferellce on Image Processing (ICIP '01), Tessaloniki, Greece, Vol. 2, 2001, pp. 681–684.        [ Links ]

2. Androutsos D., K.N. Plataniotis and Venetsanopoulos A.N., "A novel vector–basad approach to color image retrieval using a vector angular–based distance measure", Computer Vision and Image Understanding,Vol. 75, 1999, pp.46–58.        [ Links ]

3. Angulo J., Morphologie Mathématique et indexation d'images couleur. Application a la microscopie en biomédecine. Ph.D. Thesis, Centre de Morphologie Mathématique, Ecole des Mines, París, December 2003.        [ Links ]

4. Angulo J. and Serra J., "Color segmentation by ordered mergings" in Proc. of IEEE International Conference on Image Processing (ICIP'03), IEEE, Vol. 2, 125–128, Barcelona, Spain, September 2003.        [ Links ]

5. Angulo J. and Serra J., "Traitements des images de couleur en représentation luminance/saturation/teinte par norme L₁", Traitement du Signal, Vol. 21(6), 2004, 20 p.        [ Links ]

6. Beucher S. and Meyer F., "The Morphological Approach to Segmentation: The Watershed Transformation," in (E. Dollgherty Ed.), Mathematical Morphology in Image Processing, Marcel Dekker, 1992, pp. 433–481.        [ Links ]

7. Celenk M., "A color clustering technique for image segmentation", Computer Vision Graphics and Image Processing, Vol. 52, 1990, pp. 145–170.        [ Links ]

8. Demarty C.–H. and Beucher S., "Color segmentation algorithm using an HLS transformation", in (Heijmans and Roerdink Eds.), Mathematical Morphology and its Applications to Image and Signal Processing, Kluwer,1998, pp. 231–238.        [ Links ]

9. Fu K. and Mui J., "A survey on image segmentation", Pattern Recognition, Vol. 13, 1981, pp. 3–16.        [ Links ]

10.Géraud T., Strub P.Y. and Darbon J., "Color image segmentation", in Proc. of IEEE International Conference on lmage Processing (ICIP'01), IEEE, Vol. 3, 70–73, 2001.        [ Links ]

11.Grimaud M., "New measure of contrast", in Image Algebra and Morphological Image Processing III , SPIE–1760, 1992 pp. 291–305.        [ Links ]

12. Hanbury A. and Serra J., "Colour Image Analysis in 3D–polar Coordinates", in DAGM 2003, Magdeburg, Germany.        [ Links ]

13. Kurugollu F., Sankur B., and Harmanci A., "Color image segmentation using histogram multithresholding and fusion", Image and Vison Computing. Vol. 19, No. 13, 2001, pp. 915–928.        [ Links ]

14. Littman E. and Ritter E., "Colour image segmentation : a comparison of neural and statistical methods", IEEE Transactions on Neural Networks, Vol. 8, No. 1, 1997, pp. 175–185.        [ Links ]

15. Park S.H. Yun I.D. and Lee S.U., "Color image segmentation based on 3–D clustering: morphological approach", Pattern Recognition, Vol. 31, No. 8, 1998, pp. 1061–1076.        [ Links ]

16. Petrou M., L. Shafarenko and Kittler J., "Histogram–based segmentation in a perceptually uniform color space", IEEE Transactionson Image Processing, Vol. 7, No. 9, 1998, pp. 1354–1358.        [ Links ]

17. Postaire J.G., Zhang R.D. and Lecocq–Botte c., "Cluster Analysis by Binary Morphology", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 2, 1993, pp. 170– 180.        [ Links ]

18. Salembier P. and Serra J., "Flat Zones Filtering, Connected Operators, and Filters by Reconstruction", IEEE Transactions on lmage Processing, Vol. 4, No. 8, 1995, pp. 1153–1160.        [ Links ]

19. Sang H.P., D.Y. Il and Sang U.L., "Color image segmentation based on 3D clustering: morphological approach", Pattern Recognition, Vol. 31, No. 8, 1998, pp. 1061–1076. .        [ Links ]

20. Serra J., Image Analysis and Mathematical Morphology. Vol I, and Image Analysis and Mathematical Morphology. Vol II: Theoretical Advances, Academic Press, London 1982 and 1988.        [ Links ]

21. Serra J., "Espaces couleur et traitement d'images", CMM–Ecole des Mines de París, Internal Note N–34/02/MM, October 2002, 13 p.        [ Links ]

22. Serra J., "Connection, Image Segmentation and Filtering", in Proc. of XI International Computing Conference CIC'02, Mexico DF, 2002.        [ Links ]

23. Soille P., "Mmphological partitioning of multispectral images", Journal of Electronic Imaging, Vol. 5, 1996, pp. 252–265.        [ Links ]

24. Trémeau A. and Borel N., "A region growing and merging algorithm to color segmentation", Pattern Recognition, Vol. 30, No. 7, 1998, pp. 1191–1203.        [ Links ]

25. Watson A., "A new method of classification for Landsat data using the watershed algorithm", Pattern Recognition Letters, Vol. 6, 1987, pp. 15–20.        [ Links ]

26. Zugaj D. and Lattuati V., "A new approach of color images segmentation based on fusing region and edge segmentation outputs", Pattern Recognition, Vol. 31, No. 2, 1998, pp. 105–113.        [ Links ]