Architecture for Image Labelling in Real Conditions

  • Juan Manuel García Chamizo
  • Andrés Fuster Guilló
  • Jorge Azorín López
  • Francisco Maciá Pérez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2626)


A general model for the segmentation and labelling of acquired images in real conditions is proposed. These images could be obtained in adverse environmental conditions, such as faulty illumination, non-homogeneous scale, etc. The system is based on surface identification of the objects in the scene using a database. This database stores features from series of each surface perceived with successive optical parameter values: the collection of each surface perceived at successive distances, and at successive illumination intensities, etc. We propose the use of non-specific descriptors, such as brightness histograms, which could be systematically used in a wide range of real situations and the simplification of database queries by obtaining context information. Self-organizing maps have been used as a basis for the architecture, in several phases of the process. Finally, we show an application of the architecture for labelling scenes obtained in different illumination conditions and an example of a deficiently illuminated outdoor scene.


Illumination Condition Calibration Function Database Query Illumination Level Image Labelling 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Campbell, N.W., Mackeown, W.P.J., Thomas B.T., and Troscianko T.: Interpreting Image Databases by Region Classification. Pattern Recognition (Special Edition on Image Databases), 30(4):555–563, April 1997.Google Scholar
  2. 2.
    Dana, K.J., Ginneken, B., Nayar S., and Koenderink J.J.: Reflectance and Texture of Real-World Surfaces. ACM Transactions on Graphics, Vol. 18, No. 1, Pages 1–34. January 1999.CrossRefGoogle Scholar
  3. 3.
    Flusser J., and Suk T.: Degraded Image Analysis: An Invariant Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 6. June 1998.Google Scholar
  4. 4.
    Biemond, J., Lagendijk R.L., and Mersereau R.M.: Iterative Methods for Image Deblurring. Proc. IEEE. Vol. 78, pp. 856–883, 1990.CrossRefGoogle Scholar
  5. 5.
    Moik J.G.: Digital Processing of remotely sensed images. NASA SP-431, Washington DC 1980.Google Scholar
  6. 6.
    Rosenfeld, A., and Kak, A.C.: Digital picture processing. Academic Press. New York, 2nd Ed.Google Scholar
  7. 7.
    González, R.C., and Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing Company Inc. 1992.Google Scholar
  8. 8.
    Sonka, M., Hlavac, V., and Boyle, R.: Image Processing, Analysis, and Machine Vision (2nd edition). Brooks/Cole Publishing Company, 1998.Google Scholar
  9. 9.
    Shanahan, J.G., Baldwin, J.F., Thomas, B.T., Martin, T.P., Campbell, N.W., and Mirmehdi., M.: Transitioning from Recognition to Understanding in Vision using Cartesian Granule Feature Models. Additive Proceedings of the Intn’l conference of the North American Fuzzy Information Processing Society, New York, pp 710–714. NAFIPS 1999.Google Scholar
  10. 10.
    Malik J., Belongie S., Leung T., and Shi J.: Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision 43(1), 7–27, 2001.zbMATHCrossRefGoogle Scholar
  11. 11.
    Tamura, H., Mori S., and Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on SMC. 8(6): 460–473, 1978.Google Scholar
  12. 12.
    Leow, W.K., and Lai, S.Y.: Scale and orientation-invariant texture matching for image retrieval. Texture Analysis in Machine Vision. World Scientific 2000.Google Scholar
  13. 13.
    Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of IEEE. Vol 67. Pag 786–804. 1979.CrossRefGoogle Scholar
  14. 14.
    Won, C.S.: Block-based unsupervised natural image segmentation. Optical Engineering. Vol. 39(12). December 2000.Google Scholar
  15. 15.
    Bhalerao, A., and Wilson, R.: Unsupervised Image Segmentation Combining Region and Boundary Estimation. Image and Vision Computing, volume 19, No 6, pp. 353–368. 2001.CrossRefGoogle Scholar
  16. 16.
    Pujol, F., García, J.M., Fuster, A., Pujol, M., and Rizo, R.: Use of Mathematical Morphology in Real-Time Path Planning. Kybernetes (The International Journal of Systems & Cybernetics). Editorial: MCB University Press. Vol. 31 No. 1 pp. 115–124. 2001.CrossRefGoogle Scholar
  17. 17.
    García, J.M., Fuster, A., and Azorín, J.: Image Labelling in Real Conditions. Application in Scale Treatment. Advances in Systems Engineering, Signal Processing and Communications. WSEAS Press. Oct 2002.Google Scholar
  18. 18.
    Strat, T., and Fischler, M.: The Role of Context in Computer Vision. In Proceedings of the International Conference on Computer Vision, Workshop on Context-Based Vision.1995.Google Scholar
  19. 19.
    Hadjidemetriou, E., Grossberg, M.D., Nayar, S.K.: Histogram Preserving Image Transformations. International Journal of Computer Vision 45(1), 5–23. October 2001.zbMATHCrossRefGoogle Scholar
  20. 20.
    Fritzke, B.: Some Competitive Learning Methods. Draft Paper, System Biophysics, Institute for Neural Computation, Rurh-Universität Bochum, 1997.Google Scholar
  21. 21.
    Kohonen, T.: Self-Organizing Maps. Springer-Verlag, Berlin Heidelberg, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Juan Manuel García Chamizo
    • 1
  • Andrés Fuster Guilló
    • 1
  • Jorge Azorín López
    • 1
  • Francisco Maciá Pérez
    • 1
  1. 1.U.S.I. Informática Industrial y Redes de Computadores. Dpto Tecnología Informática y ComputaciónUniversidad de AlicanteAlicanteEspaña

Personalised recommendations