A System for the Acquisition, Interactive Exploration and Annotation of Stereoscopic Images

  • Karim Benzeroual
  • Mohammed Haouach
  • Christiane Guinot
  • Gilles Venturini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

We present in the paper a system that integrates all hardware and software to extract information from 3D images of skin. It is composed of a lighting equipment and stereoscopic cameras, a camera calibration algorithm that uses evolutionary principles, virtual reality equipment to visualize the images and interact with them in 3D, a set of interactive features to annotate images, to create links between them and to build a 3D hypermedia. We present an experimental study and an application of our tool on faces skin.

Keywords

Stereoscopic acquisition camera calibration genetic algorithms 3D visualization image annotation hypermedia skin relief 

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References

  1. 1.
    Alfonso, B.: Science Magazine, a publication by the AAAS vol. 308 featured Virtual Lab in their NetWatch section (2005)Google Scholar
  2. 2.
    Baeck, T., Hoffmeister, F., Schwefel, H.-P.: A Survey of Evolution Strategies. In: Proc. Fourth Int. Conf. Genetic Algorithms, pp. 2–9. Morgan Kaufmann, San Francisco (1991)Google Scholar
  3. 3.
    Bernard, A., Cohen, M., Christoph, U., Lehmann, M.D.: DermAtlas, Johns Hopkins University (2008), www.dermatlas.org
  4. 4.
    Bouguet, J.: Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/
  5. 5.
    Chalam, K.V., Jain, P., Shah, V.A., Shah, G.Y.: Evaluation of web-based annotation of ophthalmic images for multicentric clinical trials. Indian Journal of Ophthalmology 54, 126–129 (2006)CrossRefPubMedGoogle Scholar
  6. 6.
    Chambon, S., Crouzil, A.: Dense matching using correlation: new measures that are robust near occlusions. In: British Machine Vision Conference, BMVC 2003, vol. 1, pp. 143–152 (2003)Google Scholar
  7. 7.
    D’Apuzzo, N.: Modeling human faces with multiimage photogrammetry. In: Three-Dimensional Image Capture and Applications, vol. 4661, pp. 191–197 (2002)Google Scholar
  8. 8.
    Hernandez Esteban, C., Schmitt, F.: Silhouette and Stereo Fusion for 3D Object Modeling. Computer Vision and Image Understanding 96(3), 367–392 (2003)CrossRefGoogle Scholar
  9. 9.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation, 323–344 (1987)Google Scholar
  10. 10.
    Zhang, Y., Ji, Q.: Camera Calibration With Genetic Algorithms. In: IEEE International Conference on Robotics and Automation, pp. 2177–2182 (2001)Google Scholar
  11. 11.
    Zhang, Z.: A Flexible New Technique for Camera Calibration. Technical Report MSR-TR, Microsoft Research, pp. 98–71 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karim Benzeroual
    • 1
    • 2
  • Mohammed Haouach
    • 1
    • 2
  • Christiane Guinot
    • 1
    • 2
  • Gilles Venturini
    • 1
  1. 1.Computer Science LaboratoryUniversity François-Rabelais of ToursToursFrance
  2. 2.CE.R.I.E.S., Biometrics and Epidemiology unitNeuilly sur SeineFrance

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