Improved Anisotropic Gaussian Filters
| Autor: | Alex Keilmann, Michael Godehardt, Ali Moghiseh, Claudia Redenbach, Katja Schladitz |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Image Analysis and Stereology, Vol 43, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1580-3139 1854-5165 |
| DOI: | 10.5566/ias.3023 |
| Popis: | Elongated anisotropic Gaussian filters are used for the orientation estimation of fibers. In cases where computed tomography images are noisy, roughly resolved, and of low contrast, they are the method of choice even if being efficient only in virtual 2D slices. However, minor inaccuracies in the anisotropic Gaussian filters can carry over to the orientation estimation. Therefore, this paper proposes a modified algorithm for 2D anisotropic Gaussian filters and shows that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, the effectiveness of the approach is shown by applying it to real-world images of sheet molding compounds. |
| Databáze: | Directory of Open Access Journals |
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