A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
An ISODATA algorithm for straight line fitting
Pattern Recognition Letters
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new split-and-merge technique for polygonal approximation of chain coded curves
Pattern Recognition Letters
Algorithms for straight line fitting using k-means
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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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.