Easy-to-Use Object Selection by Color Space Projections and Watershed Segmentation

  • Per Holting
  • Carolina Wählby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Digital cameras are gaining in popularity, and not only experts in image analysis, but also the average users, show a growing interest in image processing. Many different kinds of software for image processing offer tools for object selection, or segmentation, but most of them require expertise knowledge, or leave too little freedom in expressing the desired segmentation. This paper presents an easy to use tool for object segmentation in color images. The amount of user interaction is minimized, and no tuning parameters are needed. The method is based on the watershed segmentation algorithm, combined with seeding information given by the user, and color space projections for optimized object edge detection. The presented method can successfully segment objects in most types of color images.


Color Image User Input Gradient Magnitude Object Segmentation Extract Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Adobe Photoshop version 7.0Google Scholar
  2. 2.
    Rother, C., Kolmogorov, V., Blake, A.: “GrabCut” – Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transactions on Graphics 23, 309–314 (2004)CrossRefGoogle Scholar
  3. 3.
    Gonzales, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    Di Zenzo, S.: A Note on the Gradient of a Multi-Image. Computer Vision. Graphics and Image Processing 33(1) (1986)Google Scholar
  5. 5.
    Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles, ch. 3.3.5. Addison-Wesley Publishing Company, Reading (1974)zbMATHGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis, ch. 4.10. Wiley-Interscience, Hoboken (1973)zbMATHGoogle Scholar
  7. 7.
    Beucher, S., Lantuèjoul, C.: Use of watershed on contour detection. In: International Workshop on Image Processing: Real-time and Motion Detection/Estimation, Rennes, France (1979)Google Scholar
  8. 8.
    Meyer, F., Beucher, S.: Morphological segmentation. Journal of Visual Communication and Image Representation 1(1), 21–46 (1990)CrossRefGoogle Scholar
  9. 9.
    Vincent, L.: Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Transactions on Image Processing 2(2), 176–201 (1993)CrossRefGoogle Scholar
  10. 10.
    Vincent, L., Soille, P.: Watersheds in Digital Spaced: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  11. 11.
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Per Holting
    • 1
  • Carolina Wählby
    • 1
  1. 1.Centre for Image AnalysisUppsala UniversitySweden

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