HK Segmentation of 3D Micro-structures Reconstructed from Focus

  • Muhammet A. Hocaoglu
  • Mustafa Unel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5226)


This paper presents an evaluation of HK segmentation on 3D micro-structures reconstructed from focus. Due to the necessity of 3D shape and surface structure information for a precise micromanipulation, shape of the object is recovered using shape from focus (SFF). In the SFF procedure, using an image sequence, composed of images captured at different focusing levels, focused image of the micro-structure and its shape are acquired. Then the resulting shape, also called range image, is used in HK segmentation method to extract surface curvature information. Experimental results show that this technique works for both synthetic and real data.


HK Segmentation 3D Micro-structures Image 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nayar, S.K., Nakagawa, Y.: Shape from Focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(8), 824–831 (1994)CrossRefGoogle Scholar
  2. 2.
    Pradeep, K.S., Rajagopalan, A.N.: Improving Shape from Focus Using Defocus Cue. IEEE Transactions on Image Processing 16(7), 1920–1925 (2007)CrossRefGoogle Scholar
  3. 3.
    Nayar, S.K., Nakagawa, Y.: Shape from focus: An Effective Approach for Rough Surfaces. In: Proceedings of International Conference on Robotics Automation, pp. 218–225 (1990)Google Scholar
  4. 4.
    Malik, A.S., Choi, T.S.: Consideration of Illumination Effects and Optimization of Window Size for Accurate Calculation of Depth Map for 3D Shape Recovery. Pattern Recognition 40, 154–170 (2007)MATHCrossRefGoogle Scholar
  5. 5.
    Helmli, F.S., Scherer, S.: Adaptive Shape from Focus with an Error Estimation in Light Microscopy. In: Second International Symposium on Image and Signal Processing and Analysis, pp. 188–193 (2001)Google Scholar
  6. 6.
    Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision. Prentice Hall, New Jersey (1998)Google Scholar
  7. 7.
    Trucco, E., Fisher, R.B.: Experiments in Curvature-Based Segmentation of Range Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 177–182 (1995)CrossRefGoogle Scholar
  8. 8.
    Besl, P.J., Jain, R.C.: Segmentation Through Variable-Order Surface Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(2), 167–192 (1988)CrossRefGoogle Scholar
  9. 9.
    Cantzler, H., Fisher, R.B.: Comparison of HK and SC Curvature Description Methods. In: Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, pp. 285–291 (2001)Google Scholar
  10. 10.
    Moreno, A.B., Sanchez, A., Velez, J.F., Diaz, F.J.: Face Recognition using 3D Surface-Extracted Descriptors. In: Proceedings of Irish Machine Vision and Image Processing Conference (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Muhammet A. Hocaoglu
    • 1
  • Mustafa Unel
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey

Personalised recommendations