Accuracy Improvements for Convolutional and Differential Distance Function Approximations
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910742937075712 |
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| author | Belyaev, Alexander Fayolle, Pierre-Alain |
| author_facet | Belyaev, Alexander Fayolle, Pierre-Alain |
| contents | Given a bounded domain, we deal with the problem of estimating the distance function from the internal points of the domain to the boundary of the domain. Convolutional and differential distance estimation schemes are considered and, for both the schemes, accuracy improvements are proposed and evaluated. Asymptotics of Laplace integrals and Taylor series extrapolations are used to achieve the improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accuracy Improvements for Convolutional and Differential Distance Function Approximations Belyaev, Alexander Fayolle, Pierre-Alain Numerical Analysis Computer Vision and Pattern Recognition Given a bounded domain, we deal with the problem of estimating the distance function from the internal points of the domain to the boundary of the domain. Convolutional and differential distance estimation schemes are considered and, for both the schemes, accuracy improvements are proposed and evaluated. Asymptotics of Laplace integrals and Taylor series extrapolations are used to achieve the improvements. |
| title | Accuracy Improvements for Convolutional and Differential Distance Function Approximations |
| topic | Numerical Analysis Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.09200 |