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Academic Journal of Computing & Information Science, 2022, 5(12); doi: 10.25236/AJCIS.2022.051206.

A Contour Interpolation Method Based on the Nearest Distance Method to Construct Auxiliary Lines


Wang Hao1, Lin Jian1, Gao Bin2

Corresponding Author:
Wang Hao

1Hunan University of Science and Technology, Xiangtan, China

2Hunan Hangrui Digital Technology Co. Ltd, Changsha, China


The regional interpolation method calculates the characteristic distance of the contour nodes to complete the node matching, divides the contour into multiple regions with consistent change trends, and constructs auxiliary lines equally within the region to complete the interpolation. Aiming at the uncertainty of the area divided by the regional interpolation method and the redundancy of the auxiliary line constructed, a contour interpolation method using the nearest distance method to construct the auxiliary line is proposed. Firstly, the Doiglas-Peucker algorithm is used to extract the feature points of the contour line, and the nearest distance method is used to construct the auxiliary line for the feature points, and then the new contour line is interpolated. The nearest distance method constructs the auxiliary line based on the distance between the feature point and the feature point. The auxiliary line can reflect the change trend of the contour line, so that the new contour line can retain the curve features of the original contour line. Compared with the regional interpolation method, the method in this paper has a single uncertainty factor and the auxiliary lines constructed are simple and efficient, which improves the interpolation efficiency. Through experimental comparison, the effectiveness of the method is verified.


Contour line, Auxiliary line, Nearest distance method, Doiglas-Peucker, Feature point

Cite This Paper

Wang Hao, Lin Jian, Gao Bin. A Contour Interpolation Method Based on the Nearest Distance Method to Construct Auxiliary Lines. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 38-46. https://doi.org/10.25236/AJCIS.2022.051206.


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