Image Indexing by Focus Map

  • Levente Kovács
  • Tamás Szirányi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3708)


Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in itself provides a somewhat higher level of features and also can serve as a basis for high level, i.e. semantic, image feature extraction and understanding. In our proposed method we use blind deconvolution for image area classification by interest regions, which is a novel use of the technique. We prove its viability for such and similar use.


indexing blind deconvolution focus map CBIR 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kundur, D., Hatzinakos, D.: Blind Image Deconvolution. IEEE Signal Processing Magazine, 43–64 (May 1996)Google Scholar
  2. 2.
    Pratt, W.K.: Digital Image Processing, 3rd edn., pp. 241–399. John Wiley & Sons, Chichester (2001)CrossRefGoogle Scholar
  3. 3.
    Czúni, L., Csordás, D.: Depth-Based Indexing and Retrieval of Photographic Images. In: García, N., Salgado, L., Martínez, J.M. (eds.) VLBV 2003. LNCS, vol. 2849, pp. 76–83. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Jefferies, S.M., Schulze, K., Matson, C.L.m., Stoltenberg, K.: Blind Deconvolution In Optical Diffusion Tomography. Optical Express 10, 46–53 (2002)Google Scholar
  5. 5.
    Jefferies, S.M., Schulze, K.J., Matson, C.L., Giffin, M., Okada, J.: Improved Blind Deconvolution Methods for Objects Imaged through Turbid Media. In: AMOS Technical Conference, Kihei HI (2002)Google Scholar
  6. 6.
    Dey, N., Blanc-Faud, L., Zimmer, C., Kam, Z., Olivo-Marin, J.C., Zerubia, J.: A Deconvolution Method For Confocal Microscopy With Total Variation Regularization. In: Proceedings of IEEE International Symposium on Biomedical Imaging (2004)Google Scholar
  7. 7.
    Chi, C.-Y., Chen, W.-T.: Maximum-Likelihood Blind Deconvolution: Non-White Bernoulli-Gaussian Case. IEEE Trans. on Geoscience and Remote Sensing 29, 5 (1991)CrossRefGoogle Scholar
  8. 8.
    Dijk, J., van Ginkel, M., van Asselt, R.J., van Vliet, L.J., Verbeek, P.W.: A New Sharpness Measure Based on Gaussian Lines And Eedges. In: Proceedings of ASCI2002, pp. 39–73 (2002)Google Scholar
  9. 9.
    Santos, A., Ortiz de Solorzano, C., Vaquero, J.J., Pena, J.M., Malpica, N., Del Pozo, F.: Evaluation Of Autofocus Functions. Jourlan of Microscopy Molecular Cytogenetic Analysis 188(3), 264–272 (1997)Google Scholar
  10. 10.
    Shaked, D., Tastl, I.: Sharpness Measure: Towards Automatic Image Enhancement. Hewlett-Packard Laboratories Technical Report HPL 2004-84 (2004)Google Scholar
  11. 11.
    Lim, S.H., Yen, J., Wu, P.: Detection of Out-Of-Focus Digital Photographs. Hewlett-Packard Laboratories Technical Report HPL 2005-14 (2005)Google Scholar
  12. 12.
    Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., Fernandez-Valdivia, J.: Diatom Autofocusing in Brightfield Microscopy: A Comparative Study. In: Proceedings of ICPR 2000, vol. 3, pp. 314–317 (2000)Google Scholar
  13. 13.
    Lucy, L.B.: An Iterative Technique For Rectification of Observed Distributions. The Astronomical Journal 79(6), 745–754 (1974)CrossRefGoogle Scholar
  14. 14.
    Richardson, W.H.: Bayesian-Based Iterative Method of Image Restoration. JOSA 62, 55–59 (1972)CrossRefGoogle Scholar
  15. 15.
    Batten, C.F., Holburn, D.M., Breton, B.C., Caldwell, N.H.M.: Sharpness Search Algorithms for Automatic Focusing in the Scanning Electron Microscope. Scanning: The Journal of Scanning Microscopies 23(2), 112–113 (2001)Google Scholar
  16. 16.
    Hanis, A., Szirányi, T.: Measuring the Motion Similarity in Video Indexing. In: Proceedings of 4th Eurasip EC-VIP-MC, Zagreb (2003)Google Scholar
  17. 17.
    Kato, Z., Ji, X., Szirányi, T., Tóth, Z., Czúni, L.: Content-Based Image Retrieval Using Stochastic Paintbrush Transformation. In: Proceedings of ICIP 2002 (2002)Google Scholar
  18. 18.
    Szirányi, T., Nemes, L., Roska, T.: Cellular Neural Network for Image Deconvolution and Enhancement: A Microscopy Toolkit. In: Proceedings of IWPIA, pp. 113–124 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Levente Kovács
    • 1
  • Tamás Szirányi
    • 2
  1. 1.Dept. of Image Processing and NeurocomputingUniversity of VeszprémVeszprémHungary
  2. 2.Analogical Comp. Lab., Comp. and Automation Research InstituteHungarian Academy of SciencesBudapestHungary

Personalised recommendations