Pattern Recognition and Image Analysis

, Volume 22, Issue 1, pp 221–227 | Cite as

Scale-space color blob and ridge detection

  • N. A. Khanina
  • E. V. Semeikina
  • D. V. Yurin
Representation, Processing, Analysis and Understanding of Images


Feature detection in color images frequently consists in image conversion from color to grayscale and then one of grayscale detectors application. This approach has a few disadvantages: some features become indistinguishable in grayscale and features ordering based on response of grayscale detector do not accord with features order of importance from human’s perception point of view. There are two essential contributions in this paper. First, the method for direct detection of blobs and ridges in color images is proposed. Second, for scale-space ridge detection we introduce a 3D non maxima suppression procedure (in two orthogonal directions) which makes ridge detection simple and easy programmable in contrast to Lindeberg’s automatic scale selection approach. The proposed algorithms also produce estimates of blobs sizes and ridges width.


color blob detection color ridge detection color line detection ridge edge feature points scale-space non maxima suppression 


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  1. 1.
    C. Tomasi and T. Kanade, “Shape and Motion from Image Streams: a Factorization Method—Part 3. Detection and Tracking of Point Features,” Tech. Report CMU-CS-91-132 (Computer Science Department, Carnegie Mellon University, Apr. 1991).Google Scholar
  2. 2.
    J. Shi and C. Tomasi, “Good Features to Track,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’94) (Seattle, June 1994), pp. 593–600.Google Scholar
  3. 3.
    C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” in Proc. 4th Alvey Vision Conf. (Manchester, 1988), Vol. 15, pp. 147–151.Google Scholar
  4. 4.
    J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. PAMI 8, 34–43 (1986).Google Scholar
  5. 5.
    D. G. Lowe, “Object Recognition from Local Scale-Invariant Features,” in Proc. ICCV (Kerkyra, Sept. 1999), pp. 1150–1157, Available from:
  6. 6.
    D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comp. 60(2), 91–110 (2004).CrossRefGoogle Scholar
  7. 7.
    T. Lindeberg, “Edge Detection and Ridge Detection with Automatic Scale Selection,” Int. J. Comp. Vision 30(2), 117–156 (1998).CrossRefGoogle Scholar
  8. 8.
    R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge Univ. Press, 2004).Google Scholar
  9. 9.
    P. Montesinos, V. Gouet, and R. Deriche, “Differential Invariants for Color Images,” in Proc.14th Int. Conf. on Pattern Recognition (Brisbane, 1998), pp. 838–840.Google Scholar
  10. 10.
    S. Di Zenzo, “A Note on the Gradient of Multi-Image,” Comput. Vision Graph. Image Processing 33, 116–125 (1986).CrossRefzbMATHGoogle Scholar
  11. 11.
    A. Cumani, “Edge Detection in Multispectral Images,” Comput. Vision, Graph. Image Processing 53(1), 40–51 (1991).zbMATHGoogle Scholar
  12. 12.
    A. Ming and H. Ma, “A Blob Detector in Color Images,” in Proc. 6th ACM Int. Conf. on Image and Video Retrieval (Amsterdam, 2007), pp. 364–370.Google Scholar
  13. 13.
    A. P. Witkin, “Scale-Space Filtering,” in Proc. 8th Int. Joint Conf. on Artificial Intelligence (Karlsruhe, Aug. 1983), pp. 1019–1022.Google Scholar
  14. 14.
    J. J. Koenderink and A. J. van Doorn, “The Structure of Images,” Biol. Cybern. 50, 363–370 (1984).CrossRefzbMATHGoogle Scholar
  15. 15.
    T. Lindeberg, Scale-Space Theory in Computer Vision (Kluwer Acad. Publ., Dordrecht, 1994).CrossRefGoogle Scholar
  16. 16.
    W. K. Pratt, Digital Image Processing: PIKS Scientific Inside, 4th ed. (Wiley-Intersci., 2007).Google Scholar
  17. 17.
    D. P. Nikolaev and S. M. Karpenko, “Color-to-Grayscale Image Transformation Preserving the Gradient Structure,” in Proc. 20th European Conf. on Modeling and Simulation (ECMS 2006) (Bonn, 2006), pp. 321–323.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  • N. A. Khanina
    • 1
  • E. V. Semeikina
    • 1
  • D. V. Yurin
    • 1
  1. 1.Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia

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