Genetic algorithms for error-bounded polygonal approximation

Yung Nien Sun, Shu Chien Huang

研究成果: Article

18 引文 斯高帕斯(Scopus)

摘要

A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu-Seneviratne algorithm, the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.

原文English
頁(從 - 到)297-314
頁數18
期刊International Journal of Pattern Recognition and Artificial Intelligence
14
發行號3
DOIs
出版狀態Published - 2000 五月 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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