Color line extraction is an important part of the segmentation process. The proposed method is the generalization of the Gradient Line Detector (GLD) to color images. The method relies on the computation of a color gradient field. Existing color gradient are not “oriented”: the gradient vector direction is defined up to π, and not up to 2π as it is for a grey-level image. An oriented color gradient which makes use of an ordering of colors is proposed. Although this ordering is arbitrary, the color gradient orientation changes from one to the other side of a line; this change is captured by the GLD. The oriented color gradient is derived from a generalization from scalar to vector: the components of the gradient are defined as a “signed” distance between weighted average colors, the sign being related to their respective order. An efficient averaging method inspired by the Gaussian gradient brings a scale parameter to the line detector. For the distance, the simplest choice is the Euclidean distance, but the best choice depends on the application. As for any feature extraction process, a post-processing is necessary: local maxima should be extracted and linked into curvilinear segments. Some preliminary results using the Euclidean distance are shown on a few images.


color line color edge color ordering Gaussian gradient 


  1. 1.
    Zhu, S.-Y., et al.: A Comprehensive Analysis of Edge Detection in Color Image Processing. Optical Engineering 38(4), 612–625 (1999)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Smolka, B., et al.: Noise Reduction and Edge Detection in Color Images. In: Smolka, B., et al. (eds.) Color Image Processing Methods and Applications, ch. 4, pp. 75–102. CRC Press, Boca Raton (2007)Google Scholar
  3. 3.
    Ruzon, Tomasi: Edge, Junction, and Corner Detection Using Color Distributions. TPAMI 23(11), 1281–1295 (2001)CrossRefGoogle Scholar
  4. 4.
    Gevers, T., Van de Weijer, J., Stokman, H.: Color feature detection. In: Color Image Processing Methods and Applications, ch. 9, pp. 75–102. CRC Press, Boca Raton (2007)Google Scholar
  5. 5.
    Christophe, E., Inglada, J.: Robust Road Extraction for High Resolution Satellite Images. In: ICIP (5), pp. 437–440 (2007)Google Scholar
  6. 6.
    Canny, J.: A computational approach to edge detection. TPAMI 8(6), 679–697 (1986)CrossRefGoogle Scholar
  7. 7.
    Stokman, H., et al.: Selection and Fusion of Colour Models for Image Feature Detection. TPAMI 29(3), 371–381 (2007)CrossRefGoogle Scholar
  8. 8.
    Lacroix, V., Acheroy, M.: Feature-Extraction Using the Constrained Gradient. ISPRS J. of Photogram. and RS 53(2), 85–94 (1998)CrossRefGoogle Scholar
  9. 9.
    Di Zenzo, S.: A Note on the Gradient of a Multi-Image. CVGIP 33(1), 116–125 (1986)zbMATHGoogle Scholar
  10. 10.
    Evans, Liu: A Morphological Gradient Approach to Color Edge Detection. IEEE Transactions on Image Processing 15(6), 1454–1463 (2006)CrossRefGoogle Scholar
  11. 11.
    Lacroix, V.: A Three-Module Strategy for Edge Detection. TPAMI 10(6), 803–810 (1988)CrossRefGoogle Scholar
  12. 12.
    Ohta, N., Robertson, A.R.: Colorimetry Fundamentals and Applications. John Wiley & Sons, Ltd., Chichester (2005)CrossRefGoogle Scholar
  13. 13.
    Kuehni, R.: Color difference formulas: An unsatisfactory state of affairs. Color Research & Application 33(4), 324–326 (2008)CrossRefGoogle Scholar
  14. 14.
    Berns, R.: Billmeyer and Saltzman’s Principles of Color Technology. John Wiley & Sons, Chichester (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vinciane Lacroix
    • 1
  1. 1.Signal and Image Centre DepartmentRoyal Military AcademyBrusselsBelgium

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