Abstract
In this work a new method for obtaining optimal polygonal approximations is presented. The new method is iterative and uses a improved version of the method proposed by Salotti. In the first iteration, the Perez method is used with a random starting point for obtaining a suboptimal polygonal approximation. In the rest of iterations, the improved Salotti method is used. The best error value obtained in the previous iterations is used as a value of pruning for the next iterations. The farthest point from the starting point, in the obtained polygonal approximation, is used as starting point in the next iteration. Tests have shown that in a small number of iterations, global optimal polygonal approximation is obtained. The results show that the computation time is significantly reduced, compared with existing methods.
This work has been developed with the support of the Research Projects called TIN2012-32952 and BROCA both financed by Science and Technology Ministry of Spain and FEDE.
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Carmona-Poyato, A., Aguilera-Aguilera, E.J., Madrid-Cuevas, F.J., López-Fernandez, D. (2015). New Method for Obtaining Optimal Polygonal Approximations. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_17
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DOI: https://doi.org/10.1007/978-3-319-19390-8_17
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