The Imalab Method for Vision Systems

  • Augustin Lux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2626)


We propose a method to construct computer vision systems using a workbench composed of a multi-faceted toolbox and a general purpose kernel. The toolbox is composed of an open set of library modules. The kernel facilitates incremental dynamic system construction. This method makes it possible to quickly develop and experiment new algorithms, it simplifies the reuse of existing program libraries, and allows to construct a variety of systems to meet particular requirements. Major strong points of our approach are: (1) Imalab is a homogeneous environment for different types of users, who share the same basic code with different interfaces and tools. (2) Integration facility: modules for various scientific domains, in particular robotics or AI research (e.g. Bayesian reasoning, symbolic learning) can be integrated automatically. (3) Multilanguage integration: the C/C++ language and several symbolic programming languages - Lisp(Scheme), Prolog, Clips - are completely integrated. We consider this an important advantage for the implementation of cognitive vision functionalities. (4) Automatic program generation, to make multi-language integration work smoothly. (5) Efficiency: library code runs without overhead.

The Imalab system is in use for several years now, and we have started to distribute it.


Vision System Source Code Image Class Garbage Collector Interactive Shell 
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.


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  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
    D.H. Ballard, C.M. Brown, J.A. Feldman. An approach to knowledge-directed image analysis. in [13].Google Scholar
  10. 10.
    Shigeru Chiba. OpenC++ 2.5 Reference Manual. University of Tsukuba.Google Scholar
  11. 11.
    V. Colin de Verdière and J. L. Crowley (1998) Visual Recognition using Local Appearance. European Conference on Computer Vision ECCV’98, Freiburg, June 1998.Google Scholar
  12. 12.
    J.L. Crowley and H. Christensen (editors). Experimental Environments for Computer Vision and Image Processing. World Scientific, Machine Perception Artificial Intelligence Series, Vol. 11, 1994.Google Scholar
  13. 13.
    A.R. Hanson, E.M. Riseman (eds.) Computer Vision Systems. Academic Press 1978.Google Scholar
  14. 14.
    Augustin Lux (2001). Tools for automatic interface generation in scheme. In 2nd workshop on Scheme and Functional Programming, Florence, Italy, September 2001.Google Scholar
  15. 15.
    J. Rasure, S. Kubica (1994). The Khoros Application Development Environment In [12].Google Scholar
  16. 16.
    J. Rasure, M. Young (1995). Cantata: Visual Programming Environment for the Khoros system. Computer Graphics, A Publication of the ACM Siggraph, 29:22–24.CrossRefGoogle Scholar
  17. 17.
    R. van Balen et al. (1994) ScilImage: A Multi-Layered Environment for Use and Development of Image Processing Systems. In [12].Google Scholar
  18. 18.
    I.T. Young, L.J. van Vliet (1995). Recursive Gaussian Filtering In SCIA’ 95.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Augustin Lux
    • 1
  1. 1.Laboratoire GRAVIR/IMAGInstitut National Polytechnique de GrenobleFrance

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