Approximation of Digital Curves using a Multi-Objective Genetic Algorithm

In this paper, a digital planar curve approximation method based on a multi-objective genetic algorithm is proposed. In this method, the optimization/exploration algorithm locates breakpoints on the digital curve by minimizing simultaneously the number of breakpoints and the approximation error. Using such an approach, the algorithm proposes a set of solutions at its end. The user may choose his own solution according to its objective. The proposed approach is evaluated on curves issued from the literature and compared successfully with many classical approaches. 1

Polygonal Approximation of Digital Curves Using a Multi-Objective Genetic Algorithm

In this paper, a polygonal approximation approach based on a multi-objective genetic algorithm is proposed. In this method, the optimization/exploration algorithm locates breakpoints on the digital curve by minimizing simultaneously the number of breakpoints and the approximation error. Using such an approach, the algorithm proposes a set of solutions at its end. This set which is called the Pareto Front in the multi objective optimization field contains solutions that represent trade-offs between the two classical quality criteria of polygonal approximation: the Integral Square Error (ISE) and the number of vertices. The user may choose his own solution according to its objective. The proposed approach is evaluated on curves issued from the literature and compared with many classical approaches. Keywords: Polygonal Approximation, Multi-Objective Optimization, Genetic Algorithm, Pareto Front.

Author manuscript, published in "ICPR (2), Hong Kong (2006)" Approximation of Digital Curves using a Multi-Objective Genetic Algorithm

In this paper, a digital planar curve approximation method based on a multi-objective genetic algorithm is proposed. In this method, the optimization/exploration algorithm locates breakpoints on the digital curve by minimizing simultaneously the number of breakpoints and the approximation error. Using such an approach, the algorithm proposes a set of solutions at its end. The user may choose his own solution according to its objective. The proposed approach is evaluated on curves issued from the literature and compared successfully with many classical approaches. 1