Abstract
In computer graphics, shape recognition is widely solved problem. The shape functions, shape distribution and Minkowski norm are the standard methods for the determination of similarity measure. In this paper, the shape functions D2, D3 and new C1 are applied to five triangular meshes of a half-sphere, a cylinder and a plane obtained from ball-bar, ring, and gauge block after trimming. In the using of optical scanners the calibration is necessary and it is done using calibration artefacts with known dimensions (according to which the calibration is executed). So, it is useful to find the algorithm, where the known dimensions are not necessary for calibration. Therefore, the aim of this paper (and the first step for finding the algorithm) is to define whether each shape function is competent to measure the similarity and whether the new shape function C1 is as good as the standard functions. To determine it, Measurement System Analysis (MSA) was used.
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Acknowledgement
The paper was supported by student grant SGS21/148/OHK2/3T/12: Applications of mathematical-geometric modelling in mechanical engineering from Grant Agency of CTU in Prague.
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Pajerová, N. (2022). Comparison of Triangular Meshes Using Shape Functions and MSA. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_22
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DOI: https://doi.org/10.1007/978-3-030-96302-6_22
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