Polygonal approximation using genetic algorithm
Shu‐Chien Huang, Yung-Nien Sun · 2002
Polygonal approximation is an important issue in pattern recognition and image processing. A new polygonal approximation method using a 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 and Chin (1989) and Ansari and Huang (1991). Some experimental results depict the superiority of the proposed approach.