A genetic algorithm with sharing for the detection of 2D geometric primitives in images

  • Evelyne Lutton
  • Patrice Martinez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1063)


We investigate the use of genetic algorithms (GAs) in the framework of image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough Transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters.

Indeed, a GA is a stochastic technique, relatively slow, but which provides with an efficient tool to search in a high dimensional space. The philosophy of the method is very similar to the Hough Transform, which is to search an optimum in a parameter space. However, we will see that the implementation is different.

The idea of using a GA for that purpose is not new, Roth and Levine [18] have proposed a method for 2D and 3D primitives in 1992. For the detection of 2D primitives, we re-implement that method and improve it mainly in three ways:
  • by using distance images instead of directly using contour images, which tends to smoothen the function to optimize,

  • by using a GA-sharing technique, to detect several image primitives in the same step,

  • by applying some recent theoretical results on GAs (about mutation probabilities) to reduce convergence time.


Genetic Algorithms Image Primitive extraction Sharing Hough Transform 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    E. Aarts and P. Van Laarhoven. Simulated annealing: a pedestrian review of the theory and some applications. AI Series F30, NATO.Google Scholar
  2. 2.
    L. B. Booker. Intelligent behavior as an adaptatition to the task environment. PhD thesis, University of Michigan, Logic of Computers Group, 1982.Google Scholar
  3. 3.
    Gunilla Borgefors. Distance transformations in arbitrary dimensions. Computer Vision, Graphics, and Image Processing, 27:321–345, 1984.Google Scholar
  4. 4.
    A. Bihian C. Lemarechal, J.J. Strodiot. On a bundle algorithm for nonsmooth optimization, pages 245–282. Academic Press, 1881. Non-Linear Programming 4, Mangasarian, Meyer, Robinson, Editeurs.Google Scholar
  5. 5.
    T. E. Davis and J. C. Principe. A Simulated Annealing Like Convergence Theory for the Simple Genetic Algorithm. In Proceedings of the Fourth International Conference on Genetic Algorithm, pages 174–182, 1991. 13–16 July.Google Scholar
  6. 6.
    S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(6):712–741, November 1984.Google Scholar
  7. 7.
    D. A. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, January 1989.Google Scholar
  8. 8.
    David E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Genetic Algorithms and their Applications, pages 41–49, Hillsdale, New Jersey, 1987. Lawrence Erlbaum Associates.Google Scholar
  9. 9.
    J. H. Holland. Adaptation in Natural and Artificial System. Ann Arbor, University of Michigan Press, 1975.Google Scholar
  10. 10.
    J. Horn. Finite Markov Chain Analysis of Genetic Algorithms with Niching. IlliGAL Report 93002, University of Illinois at Urbana Champaign, February 1993.Google Scholar
  11. 11.
    P. V. C. Hough. A new method and means for recognizing complex pattern, 1962. U. S. Patent 3,0690,654.Google Scholar
  12. 12.
    A. Van Dam J.D. Foley. Computer graphics — principles and practise.Google Scholar
  13. 13.
    V. F. Leavers. The dynamic generalized hough transform for the concurrent detection of circles and ellipses. In Progress in Image Analysis and Processing II. 6th International Conference on IASP, 1991.Google Scholar
  14. 14.
    Evelyne Lutton, Henri Maitre, and Jaime Lopez-Krahe. Determination of vanishing points using hough transform. PAMI, 16(4):430–438, April 1994.Google Scholar
  15. 15.
    Evelyne Lutton and Patrice Martinez. A genetic algorithm for the detection of 2d geometric primitives in images. Research Report 2110, INRIA, November 1993.Google Scholar
  16. 16.
    R. Otten and L. Van Ginneken. Annealing: the algorithm. Technical Report RC 10861, March 1984.Google Scholar
  17. 17.
    G. Roth and M. D. Levine. Geometric Primitive Extraction Using a Genetic Algorithm. In IEEE Computer Society Conference on CV and PR, pages 640–644, 1992.Google Scholar
  18. 18.
    Gerhard Roth and Martin D. Levine. Extracting geometric primitives. CVGIP: Image Understanding, 58(1):1–22, July 1993.Google Scholar
  19. 19.
    Frank Tong and Ze-Nian Li. On improving the accuracy of line extraction in hough space. International journal Of Pattern Recognition and Artificial Intelligence, 6(5):831–847, 1992.Google Scholar
  20. 20.
    J. Lévy Véhel and E. Lutton. Optimization of fractal functions using genetic algorithms. In Fractal 93, 1993. London.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Evelyne Lutton
    • 1
  • Patrice Martinez
    • 1
  1. 1.INRIA-RocquencourtLe Chesnay CedexFrance

Personalised recommendations