A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Detection of significant points and polygonal approximation of digitized curves
Pattern Recognition Letters
An algorithm for detection of dominant points and polygonal approximation of digitized curves
Pattern Recognition Letters
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Another look at the dominant point detection of digital curves
Pattern Recognition Letters
A new method for polygonal approximation using genetic algorithms
Pattern Recognition Letters
A new circle/ellipse detector using genetic algorithms
Pattern Recognition Letters
An efficient algorithm for the optimal polygonal approximation of digitized curves
Pattern Recognition Letters
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Pattern Recognition Letters
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Approximation of digital curves with line segments and circular arcs using genetic algorithms
Pattern Recognition Letters
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
IEEE Transactions on Computers
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective learning via genetic algorithms
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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.