Polygonal approximation using genetic algorithm

Shu Chien Huang, Yung Nien Sun

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Polygonal approximation is an important issue in pattern recognition and image processing. A new polygonal approximation method using genetic algorithm is proposed. Genetic algorithms are search algorithms based on the mechanisms of natural selection and natural genetics. The chromosome is used to represent an approximated polygon and is represented by a binary string. Each bit, called gene, represents a curve point. A gene with value 1 indicates that the corresponding curve point is a breakpoint of the approximated polygon. The objective function is defined as the total arc-to-chord deviation between the curve and the polygon. The proposed method is compared to two existing methods proposed by Teh-Chin [5] and Ansari-Huang [6]. Some experimental results depict the superiority of the proposed approach.

Original languageEnglish
Pages469-474
Number of pages6
Publication statusPublished - 1996 Jan 1
EventProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn
Duration: 1996 May 201996 May 22

Other

OtherProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96
CityNagoya, Jpn
Period96-05-2096-05-22

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Huang, S. C., & Sun, Y. N. (1996). Polygonal approximation using genetic algorithm. 469-474. Paper presented at Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96, Nagoya, Jpn, .