| Autor: |
Diego Patino, John W. Branch |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 9, Pp 65466-65481 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3072933 |
| Popis: |
We present the CPMA, a new method for medial axis pruning with noise robustness and equivariance to isometric transformations. The CPMA leverages the discrete cosine transform to create smooth versions of a shape $\Omega $ . We use the smooth shapes to compute a score function $\mathcal {F}_{\Omega }$ that filters out spurious branches from the medial axis of the original shape $\Omega $ . Our method generalizes to $n$ -dimensional shapes given the properties of the Discrete Cosine Transform. We extensively compare with state-of-the-art pruning methods to highlight the CPMA’s noise robustness and isometric equivariance. We conducted experiments using two 2D datasets — Kimia216 and Animal2000 — and one 3D dataset — the Groningen benchmark. We found that our pruning approach achieves competitive results and yields stable medial axes even in scenarios with significant contour perturbations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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