Polygonal Approximation Using Genetic Algorithms

  • Peng-Yeng Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)


In this paper, three polygonal approximation approaches using genetic algorithms are proposed. The first approach approximates the digital curve by minimizing the number of sides of the polygon and the approximation error should be less than a prespecified tolerance value. The second approach minimizes the approximation error by searching for a polygon with a given number of sides. The third approach, which is more practical, determines the approximating polygon automatically without any given condition. Moreover, a learning strategy for each of the proposed genetic algorithm is presented to improve the results. The experimental results show that the proposed approaches have better performances than do the existing methods.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldberg, D.E.: Genetic Algorithms: Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989.zbMATHGoogle Scholar
  2. 2.
    Held, A., Abe, K., Arcelli, C.: Towards a hierarchical contour description via dominant point detection. IEEE Trans. Syst., Man, Cybern. 24 (1994) 942–949.CrossRefGoogle Scholar
  3. 3.
    Phillips, T.Y,. Rosenfeld, A.: An ISODATA algorithm for straight line fitting. Pattern Recognition Lett. 7 (1988) 291–297.CrossRefGoogle Scholar
  4. 4.
    Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing 1 (1972) 244–256.CrossRefGoogle Scholar
  5. 5.
    Ray, B.K., Ray, K.S.: Determination of optimal polygon from digital curve using L1 norm. Pattern Recognition 26 (1993) 505–509.CrossRefGoogle Scholar
  6. 6.
    Ray, B.K., Ray, K.S.: A new split-and-merge technique for polygonal approximation of chain coded curves. Pattern Recognition Lett. 16 (1995) 161–169.CrossRefGoogle Scholar
  7. 7.
    Sato, Y.: Piecewise linear approximation of plane curves by perimeter optimization. Pattern Recognition 25 (1992) 1535–1543.CrossRefGoogle Scholar
  8. 8.
    Singh, M., Chatterjee, A., Chaudhury, S.: Matching structural shape descriptions using genetic algorithms. Pattern Recognition 30 (1997) 1451–1462.CrossRefGoogle Scholar
  9. 9.
    Teh, C.H., Chin, R.T.: On the detection of dominant points on digital curves. IEEE Trans. Pattern Anal. Machine Intell. 11 (1989) 859–872.CrossRefGoogle Scholar
  10. 10.
    Wall, K., Danielsson, P.E.: A fast sequential method for polygonal approximation of digitized curves. Computer Vision, Graphics, and Image Processing 28 (1984) 220–227.CrossRefGoogle Scholar
  11. 11.
    Yin, P.Y.: “Algorithms for straight line fitting using K-means,” Pattern Recognition Lett. 19 (1998) 31–41.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Peng-Yeng Yin
    • 1
  1. 1.Department of Information ManagementMing Chuan UniversityTaipeiTaiwan

Personalised recommendations